Valid HTML 4.0! Valid CSS!
%%% -*-BibTeX-*-
%%% ====================================================================
%%%  BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.64",
%%%     date            = "19 October 2024",
%%%     time            = "15:27:45 MDT",
%%%     filename        = "ijig.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "https://www.math.utah.edu/~beebe",
%%%     checksum        = "54965 28215 135746 1344900",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "BibTeX; bibliography; International Journal
%%%                        of Image and Graphics (IJIG)",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE bibliography of the
%%%                        International Journal of Image and Graphics
%%%                        (IJIG) (CODEN ????, ISSN 0219-4678), published by
%%%                        World Scientific.
%%%
%%%                        Publication began with volume 1, number 1, in
%%%                        January 2001.
%%%
%%%                        The journal has a World-Wide Web site at
%%%
%%%                            http://ejournals.wspc.com.sg/ijig/ijig.shtml
%%%
%%%                        At version 1.64, the year coverage looked
%%%                        like this:
%%%
%%%                             2001 (  39)    2009 (  33)    2017 (  25)
%%%                             2002 (  38)    2010 (  33)    2018 (  25)
%%%                             2003 (  37)    2011 (  33)    2019 (  25)
%%%                             2004 (  37)    2012 (  30)    2020 (  37)
%%%                             2005 (  42)    2013 (  32)    2021 (  61)
%%%                             2006 (  39)    2014 (  22)    2022 (  62)
%%%                             2007 (  43)    2015 (  27)    2023 (  76)
%%%                             2008 (  36)    2016 (  23)    2024 (  51)
%%%
%%%                             Article:        906
%%%
%%%                             Total entries:  906
%%%
%%%                        Data for the bibliography has been collected
%%%                        from the journal Web site.
%%%
%%%                        Numerous errors in the sources noted above
%%%                        have been corrected.   Spelling has been
%%%                        verified with the UNIX spell and GNU ispell
%%%                        programs using the exception dictionary
%%%                        stored in the companion file with extension
%%%                        .sok.
%%%
%%%                        BibTeX citation tags are uniformly chosen
%%%                        as name:year:abbrev, where name is the
%%%                        family name of the first author or editor,
%%%                        year is a 4-digit number, and abbrev is a
%%%                        3-letter condensation of important title
%%%                        words. Citation tags were automatically
%%%                        generated by software developed for the
%%%                        BibNet Project.
%%%
%%%                        In this bibliography, entries are sorted in
%%%                        publication order, using ``bibsort -byvolume''.
%%%
%%%                        The checksum field above contains a CRC-16
%%%                        checksum as the first value, followed by the
%%%                        equivalent of the standard UNIX wc (word
%%%                        count) utility output of lines, words, and
%%%                        characters.  This is produced by Robert
%%%                        Solovay's checksum utility.",
%%%  }
%%% ====================================================================
@Preamble{
    "\ifx \undefined \TM \def \TM {${}^{\sc TM}$} \fi"
}

%%% ====================================================================
%%% Acknowledgement abbreviations:
@String{ack-nhfb = "Nelson H. F. Beebe,
                    University of Utah,
                    Department of Mathematics, 110 LCB,
                    155 S 1400 E RM 233,
                    Salt Lake City, UT 84112-0090, USA,
                    Tel: +1 801 581 5254,
                    FAX: +1 801 581 4148,
                    e-mail: \path|beebe@math.utah.edu|,
                            \path|beebe@acm.org|,
                            \path|beebe@computer.org| (Internet),
                    URL: \path|https://www.math.utah.edu/~beebe/|"}

%%% ====================================================================
%%% Journal abbreviations:
@String{j-INT-J-IMAGE-GRAPHICS = "International Journal of Image and Graphics
                                  (IJIG)"}

%%% ====================================================================
%%% Bibliography entries:
@Article{Magnenat-Thalmann:2001:DSC,
  author =       "N. Magnenat-Thalmann and P. Volino and L. Moccozet",
  title =        "Designing and Simulating Clothes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "1--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hong:2001:IST,
  author =       "P. Hong and Z. Wen and T. S. Huang",
  title =        "{iFACE}: a {$3$D} Synthetic Talking Face",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "19--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nakamae:2001:OPR,
  author =       "E. Nakamae",
  title =        "An Overview of Photo-Realism for Outdoor Scenes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "27--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2001:CIB,
  author =       "J. Li and H.-Y. Shum and Y.-Q. Zhang",
  title =        "On the Compression of Image Based Rendering Scene: a
                 Comparison among Block, Reference and Wavelet Coders",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "45--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hanjalic:2001:RAV,
  author =       "A. Hanjalic and R. L. Lagendijk and J. Biemond",
  title =        "Recent Advances in Video Content Analysis: From Visual
                 Features to Semantic Video Segments",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "63--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Feng:2001:CBR,
  author =       "D. Feng",
  title =        "Content-Based Retrieval of Multimedia Information",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "83--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wayman:2001:FBA,
  author =       "J. L. Wayman",
  title =        "Fundamentals of Biometric Authentication
                 Technologies",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "93--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bouvier:2001:TTO,
  author =       "E. Bouvier and E. Gobbetti",
  title =        "{TOM}: Totally Ordered Mesh a Multiresolution
                 Structure for Time Critical Graphics Applications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "115--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shu:2001:APV,
  author =       "W. Shu and G. Rong and Z. Bian and D. Zhang",
  title =        "Automatic Palmprint Verification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "135--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Luo:2001:RCD,
  author =       "Y. Luo and R. Galli and D. S{\'a}nchez and A. Bennasar
                 and J. Forn{\'e}s and J. C. Serra and J. M. Hu{\'e}scar
                 and J. Gay{\`a}",
  title =        "A Remote Cooperative Design System Using Interactive
                 {$3$D} Graphics",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "1",
  pages =        "153--??",
  month =        jan,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:38 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pal:2001:FIP,
  author =       "S. K. Pal",
  title =        "Fuzzy Image Processing and Recognition: Uncertainty
                 Handling and Applications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "169--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yan:2001:HFI,
  author =       "H. Yan",
  title =        "Human Face Image Processing Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "197--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gavrilova:2001:CLI,
  author =       "M. Gavrilova and J. Rokne",
  title =        "Computing Line Intersections",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "217--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chiang:2001:SVC,
  author =       "T. Chiang and Y.-Q. Zhang",
  title =        "Stereoscopic Video Coding Using a Fast and Robust
                 Affine Motion Search",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "231--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Oh:2001:ESQ,
  author =       "K.-M. Oh and J.-D. Choi and C.-S. Lee and C.-J. Park
                 and E.-T. Lee",
  title =        "An Efficient and Simple Quad Edge Conversion of
                 Polygonal Mainfold Objects",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "251--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Heng:2001:INV,
  author =       "P.-A. Heng and H. Sun and K.-W. Chen and T.-T. Wong",
  title =        "Interactive Navigation of Virtual Vessel Tracking with
                 {$3$D} Intelligent Scissors",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "273--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cai:2001:MPP,
  author =       "J. Cai and Z.-Q. Liu",
  title =        "{Markov} Process in Pattern Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "287--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tadamura:2001:FRW,
  author =       "K. Tadamura and X. Qin and G. Jiao and E. Nakamae",
  title =        "Fast Rendering Water Surface for Outdoor Scenes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "313--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pan:2001:LDM,
  author =       "Z. Pan and M. Zhang and K. Zhou and C. Cheng and J.
                 Shi",
  title =        "Level of Detail and Multi-Resolution Modeling for
                 Virtual Prototyping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "329--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Amin:2001:PSC,
  author =       "A. Amin and R. Shiu",
  title =        "Page Segmentation and Classification Utilizing
                 Bottom-Up Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "345--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rodriguez:2001:GAD,
  author =       "W. Rodriguez and M. Last and A. Kandel and H. Bunke",
  title =        "Geometric Approach to Data Mining",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "2",
  pages =        "363--??",
  month =        apr,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Worring:2001:IRC,
  author =       "M. Worring and T. Gevers",
  title =        "Interactive Retrieval of Color Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "387--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jaimes:2001:LSV,
  author =       "A. Jaimes and S.-F. Chang",
  title =        "Learning Structured Visual Detectors from User Input
                 at Multiple Levels",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "415--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ngo:2001:RAC,
  author =       "C.-W. Ngo and T.-C. Pong and H.-J. Zhang",
  title =        "Recent Advances in Content-Based Video Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "445--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lienhart:2001:RTD,
  author =       "R. Lienhart",
  title =        "Reliable Transition Detection in Videos: a Survey
                 and Practitioner's Guide",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "469--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dimitrova:2001:VCU,
  author =       "N. Dimitrova and L. Agnihotri and G. Wei",
  title =        "Video Classification Using Object Tracking",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "487--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lin:2001:VCR,
  author =       "T. Lin and H. J. Zhang and Q.-Y. Shi",
  title =        "Video Content Representation for Shot Retrieval and
                 Scene Extraction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "507--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pereira:2001:MSM,
  author =       "F. Pereira and R. Koenen",
  title =        "{MPEG-7}: a Standard for Multimedia Content
                 Description",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "527--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wu:2001:MTD,
  author =       "P. Wu and Y. Choi and Y. M. Ro and C. S. Won",
  title =        "{MPEG-7} Texture Descriptors",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "3",
  pages =        "547--??",
  month =        jul,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Feb 26 12:00:39 MST 2002",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2001:AIG,
  author =       "W. Liu and T. Xin and Y. Xu and H. Shum and H.
                 Zhong",
  title =        "Artistic Image Generation by Deviation Mapping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "565--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2001:DCS,
  author =       "G. Li and X. Li and H. Li",
  title =        "Discrete Clothoid Spline Surfaces on Open Meshes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "575--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prasad:2001:TTC,
  author =       "M. V. N. K. Prasad and K. K. Shukla",
  title =        "Tree Triangular Coding Image Compression Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "591--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Parker:2001:RSD,
  author =       "J. R. Parker and J. Pivovarov",
  title =        "Recognizing Symbols by Drawing Them",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "605--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2001:EQD,
  author =       "B. P. Kumar and P. Gupta and C. J. Hwang",
  title =        "An Efficient Quadtree Datastructure for Neighbor
                 Finding Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "619--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gavrilova:2001:TAC,
  author =       "M. L. Gavrilova and M. H. Alsuwaiyel",
  title =        "Two Algorithms for Computing the {Euclidean} Distance
                 Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "635--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Stetten:2001:AFC,
  author =       "G. D. Stetten and R. Drezek",
  title =        "Active {Fourier} Contour Applied to Real Time {$3$D}
                 Ultrasound of the Heart",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "647--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tamburo:2001:GOP,
  author =       "R. J. Tamburo and G. D. Stetten",
  title =        "Gradient-Oriented Profiles for Boundary
                 Parameterization and Their Application to Core Atoms
                 Towards Shape Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "659--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Suri:2001:MSG,
  author =       "J. Suri and D. Wu and L. Reden and J. Gao and S. Singh
                 and S. Laxminarayan",
  title =        "Modeling Segmentation Via Geometric Deformable
                 Regularizers, {PDE} and Level Sets in Still and Motion
                 Imagery: a Revisit",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "681--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2001:AI,
  author =       "Anonymous",
  title =        "Author Index",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "1",
  number =       "4",
  pages =        "735--??",
  month =        oct,
  year =         "2001",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lee:2002:AIV,
  author =       "J. Lee",
  title =        "{ABSolute}: An Information Visualization System for
                 Decision Support in Sourcing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "1--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kitts:2002:LSM,
  author =       "B. Kitts and K. Hetherington-Young and M. Vrieze",
  title =        "Large-Scale Mining, Discovery and Visualization of
                 {WWW} User Clickpaths",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "21--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Unwin:2002:DMG,
  author =       "A. R. Unwin and H. Hofmann and A. F. X. Wilhelm",
  title =        "Direct Manipulation Graphics for Data Mining",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "49--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fischer:2002:AID,
  author =       "S. Fischer and H. Bunke",
  title =        "Automatic Identification of Diatoms Using Visual
                 Human-Interpretable Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "67--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pratt:2002:SPC,
  author =       "K. B. Pratt and E. Fink",
  title =        "Search for Patterns in Compressed Time Series",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "89--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Last:2002:PBA,
  author =       "M. Last and A. Kandel",
  title =        "Perception-Based Analysis of Engineering Experiments
                 in the Semiconductor Industry",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "107--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Poulet:2002:FVV,
  author =       "F. Poulet",
  title =        "Full-View: a Visual Data-Mining Environment",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "127--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Solka:2002:VFA,
  author =       "J. L. Solka and C. E. Priebe and B. T. Clark",
  title =        "A Visualization Framework for the Analysis of
                 Hyperdimensional Data",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "1",
  pages =        "145--??",
  month =        jan,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ren:2002:MDT,
  author =       "Y. Ren and C. S. Chua and Y. K. Ho",
  title =        "Motion Detection from Time-Varied Background",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "163--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kanatani:2002:MSS,
  author =       "K. Kanatani",
  title =        "Motion Segmentation by Subspace Separation: Model
                 Selection and Reliability Evaluation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "179--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2002:SVW,
  author =       "L. Wang and K. L. Chan and X.-J. Xiong",
  title =        "A Sub-Vector Weighting Scheme for Image Retrieval with
                 Relevance Feedback",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "199--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Adams:2002:FBA,
  author =       "B. Adams and C. Dorai and S. Venkatesh",
  title =        "Finding the Beat: An Analysis of the Rhythmic Elements
                 of Motion Pictures",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "215--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Luo:2002:EG,
  author =       "B. Luo and E. Hancock and R. Wilson",
  title =        "Eigenspaces for Graphs",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "247--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hild:2002:RSS,
  author =       "M. Hild and K. Nishijima",
  title =        "Reconstruction of {$3$D} Space Structure with a
                 Rotational Imaging System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "269--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2002:PAB,
  author =       "Y. Liu and C.-K. Wu and H.-T. Tsui",
  title =        "A Practical Approach for {$3$D} Building Modeling from
                 Uncalibrated Video Sequences",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "287--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tamaki:2002:CDI,
  author =       "T. Tamaki and T. Yamamura and N. Ohnishi",
  title =        "Correcting Distortion of Image by Image Registration
                 with the Implicit Function Theorem",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "309--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Iwata:2002:DOH,
  author =       "A. Iwata and K. Kato and K. Yamamoto",
  title =        "The Detection of Obstacles by the Horizon View
                 Camera",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "331--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tissainayagam:2002:PMA,
  author =       "P. Tissainayagam and D. Suter",
  title =        "Performance Measures for Assessing Contour Trackers",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "2",
  pages =        "343--??",
  month =        apr,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2002:ROM,
  author =       "Yaming Wang and George Baciu",
  title =        "Robust object matching using a modified version of the
                 {Hausdorff} measure",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "361--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:36:43 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{ElBadawy:2002:SBI,
  author =       "Ossama {El Badawy} and Mohamed Kamel",
  title =        "Shape-based image retrieval applied to trademark
                 images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "375--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:36:52 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2002:OOB,
  author =       "Taehyung Wang and Phillip C. Y. Sheu",
  title =        "An object-oriented {BSP} tree algorithm for hidden
                 surface removal",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "395--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:37:19 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kate:2002:TAA,
  author =       "Rohit Jaivant Kate and Prem Kalra and Subhashis
                 Banerjee",
  title =        "Towards an automatic approach for view-dependent
                 geometry",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "413--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:37:26 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bao:2002:IRB,
  author =       "Paul Bao and Sung-Wai Hong",
  title =        "Image restoration based on generalized finite automata
                 encoded edge preserving regularization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "425--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:37:33 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2002:IER,
  author =       "Y. J. Zhang",
  title =        "Image engineering and related publications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "441--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ablameyko:2002:CSI,
  author =       "S. Ablameyko and V. Bereishik and M. Homenko and D.
                 Lagunovsky and N. Paramonova and O. Patsko",
  title =        "A complete system for interpretation of color maps",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "453--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:37:39 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{You:2002:SSB,
  author =       "Jane You and David Zhang",
  title =        "Smart sensor: an on-board image processing system for
                 real-time remote sensing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "3",
  pages =        "481--??",
  month =        jul,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:37:44 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2002:E,
  author =       "Zhi-Qiang Liu",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "501--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pu:2002:NNB,
  author =       "Her-Chang Pu and Chin-Teng Lin",
  title =        "A neural-network-based image resolution enhancement
                 scheme for image resizing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "503--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:37:57 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2002:ASS,
  author =       "Zhi-Qiang Liu",
  title =        "Adaptive subspace self-organizing map and its
                 applications in face recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "519--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shang:2002:RFS,
  author =       "Changjing Shang and Qiang Shen",
  title =        "Rough feature selection for neural network based image
                 classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "541--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:05 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Schenker:2002:FCG,
  author =       "Adam Schenker and Mark Last and Horst Bunke and
                 Abraham Kandel",
  title =        "Fuzzy clustering with genetically adaptive scaling",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "557--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:10 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Miyamoto:2002:FMM,
  author =       "Sadaaki Miyamoto and Arnold C. Alanzado",
  title =        "Fuzzy $c$-means and mixture distribution models in the
                 presence of noise clusters",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "573--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:11 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wachs:2002:CFS,
  author =       "Juan Wachs and Helman Stern and Mark Last",
  title =        "Color face segmentation using a fuzzy min-max neural
                 network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "587--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:11 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Soodamani:2002:FHT,
  author =       "R. Soodamani and Z. Q. Liu",
  title =        "A fuzzy {Hough} transform approach to shape
                 description",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "603--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:11 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Loia:2002:FRC,
  author =       "Vincenzo Loia and Witold Pedrycz and Salvatore
                 Sessa",
  title =        "Fuzzy relation calculus in the compression and
                 decompression of fuzzy relations",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "617--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:12 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ke:2002:MST,
  author =       "Shih-Hao Ke and Tsu-Tian Lee",
  title =        "A multi-scale two-step fast search algorithm for block
                 motion estimation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "633--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:12 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kameyama:2002:CRM,
  author =       "Keisuke Kameyama and Kazuo Toraichi and Yukio
                 Kosugi",
  title =        "Constructive relaxation matching involving dynamical
                 model switching and its application to shape matching",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "655--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:12 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2002:AIV,
  author =       "Anonymous",
  title =        "Author index volume 2 (2002)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "2",
  number =       "4",
  pages =        "669--??",
  month =        oct,
  year =         "2002",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2003:Ea,
  author =       "Anonymous",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "1--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lang:2003:FSS,
  author =       "Christian A. Lang and Ambuj K. Singh",
  title =        "Faster Similarity Search for Multimedia Data Via Query
                 Transformations",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "3--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:13 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Park:2003:SBS,
  author =       "Sanghyun Park and Wesley W. Chu",
  title =        "Similarity-Based Subsequence Search in Image Sequence
                 Databases",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "31--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:13 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Oria:2003:VPV,
  author =       "Vincent Oria and M. Tamer {\"O}zsu",
  title =        "Views or Points of View on Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "55--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:13 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chen:2003:FAF,
  author =       "Longbin Chen and Baogang Hu and Lei Zhang and Mingjing
                 Li and Hongjiang Zhang",
  title =        "Face Annotation for Family Photo Album Management",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "81--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:14 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prabhakar:2003:MLJ,
  author =       "Sunil Prabhakar and Rahul Chari",
  title =        "Minimizing Latency and Jitter for Large-Scale
                 Multimedia Repositories Through Prefix Caching",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "95--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:14 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2003:CBI,
  author =       "Zhiyong Wang and Zheru Chi and Dagan Feng and Ah Chung
                 Tsoi",
  title =        "Content-Based Image Retrieval with Relevance Feedback
                 Using Adaptive Processing of Tree-Structure Image
                 Representation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "119--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:14 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cucchiara:2003:SVT,
  author =       "Rita Cucchiara and Costantino Grana and Andrea
                 Prati",
  title =        "Semantic Video Transcoding Using Classes of
                 Relevance",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "145--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Doulamis:2003:ECB,
  author =       "Anastasios Doulamis and Nikolaos Doulamis and Theodora
                 Varvarigou",
  title =        "Efficient Content-Based Image Retrieval Using Fuzzy
                 Organization and Optimal Relevance Feedback",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "171--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tangelder:2003:PMR,
  author =       "Johan W. H. Tangelder and Remco C. Veltkamp",
  title =        "Polyhedral Model Retrieval Using Weighted Point Sets",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "1",
  pages =        "209--??",
  month =        jan,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2003:Eb,
  author =       "Anonymous",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "231--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yilmaz:2003:IBI,
  author =       "Ula{\c{s}} Yilmaz and Adem Yasar M{\"u}layim and
                 Volkan Atalay",
  title =        "An Image-Based Inexpensive {$3$D} Scanner",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "235--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{El-Sana:2003:VDR,
  author =       "Jihad El-Sana and Neta Sokolovsky",
  title =        "View-Dependent Rendering for Large Polygonal Models
                 over Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "265--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cai:2003:SPA,
  author =       "Kangying Cai and Wencheng Wang and Guangzheng Fei and
                 Enhua Wu",
  title =        "A Single-Pass Approach to Adaptive Simplification of
                 Out-of-Core Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "291--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Xu:2003:FSB,
  author =       "Weiwei Xu and Zhigeng Pan and Mingmin Zhang",
  title =        "Footprint Sampling-Based Motion Editing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "311--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2003:NRB,
  author =       "Wenyu Liu and Hua Li and Guangxi Zhu",
  title =        "Non-Rigid Body Interpolation Based on Generalized
                 Morphologic Morphing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "325--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arya:2003:PFA,
  author =       "Ali Arya and Babak Hamidzadeh",
  title =        "Personalized Face Animation in Showface System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "345--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Geng:2003:PUI,
  author =       "Weidong Geng and Wolfgang Strauss and Monika
                 Fleischmann and Vladimir Elistratov and Marina Kolesnik",
  title =        "Perceptual User Interface in Virtual Shopping
                 Environment",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "365--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wan:2003:OTA,
  author =       "Huagen Wan and Shuming Gao and Qunsheng Peng and Yiyu
                 Cai",
  title =        "Optimization Techniques for Assembly Planning of
                 Complex Models in Large-Scale Virtual Environments",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "2",
  pages =        "379--??",
  month =        apr,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2003:Ec,
  author =       "Anonymous",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "399--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ailisto:2003:RFI,
  author =       "Heikki Ailisto and Mikko Lindholm and Pauli
                 Tikkanen",
  title =        "A Review of Fingerprint Image Enhancement Methods",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "401--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tico:2003:RAS,
  author =       "Marius Tico and Pauli Kuosmanen",
  title =        "A Remote Authentication System Using Fingerprints",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "425--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Konvalinka:2003:VSF,
  author =       "Ira Konvalinka",
  title =        "Verification Speed in Fingerprint-based Biometric
                 Systems",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "447--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Miao:2003:SHF,
  author =       "Jun Miao and Hong Liu and Wen Gao and Hongming Zhang
                 and Gang Deng and Xilin Chen",
  title =        "A System for Human Face and Facial Feature Location",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "461--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2003:MBA,
  author =       "Zhi-Qiang Liu and Jessica Y. Guo",
  title =        "A Model-based Approach to Hair Region Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "481--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sun:2003:DSC,
  author =       "Jun Sun and Wenyuan Wang and Qing Zhuo and Chengyuan
                 Ma",
  title =        "Discriminatory Sparse Coding and Its Application to
                 Face Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "503--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Schuckers:2003:UBB,
  author =       "Michael E. Schuckers",
  title =        "Using the Beta-Binomial Distribution to Assess
                 Performance of a Biometric Identification Device",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "3",
  pages =        "523--??",
  month =        jul,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2003:Ed,
  author =       "Anonymous",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "531--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Takizawa:2003:RML,
  author =       "Hotaka Takizawa and Kanae Shigemoto and Shinji
                 Yamamoto and Tohru Matsumoto and Yukio Tateno and
                 Takeshi Iinuma and Mitsuomi Matsumoto",
  title =        "A Recognition Method of Lung Nodule Shadows in {X}-Ray
                 {CT} Images Using {$3$D} Object Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "533--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pratikakis:2003:RMD,
  author =       "Ioannis Pratikakis and Christian Barillot and Pierre
                 Hellier and Etienne Memin",
  title =        "Robust Multiscale Deformable Registration of {$3$D}
                 Ultrasound Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "547--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Haraguchi:2003:TDR,
  author =       "Ryo Haraguchi and Naozo Sugimoto and Shigeru Eiho and
                 Yoshio Ishida",
  title =        "Three Dimensional Reconstruction of Coronary Arteries
                 by Using Registration and Texture-Mapping onto
                 Epicardial Surface on Nuclear {$3$D} Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "567--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2003:EDE,
  author =       "Jiahui Wang and Hideo Saito and Shinji Ozawa and
                 Tomohiro Kuwahara and Toyonobu Yamashita and Motoji
                 Takahashi",
  title =        "Extraction of Dermo-Epidermal Surface from {$3$D}
                 Volumetric Images of Human Skin",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "589--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Owada:2003:ECC,
  author =       "Shigeru Owada and Yoshihisa Shinagawa and Frank
                 Nielsen",
  title =        "Enumeration of Contour Correspondence",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "609--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Oshiro:2003:KGO,
  author =       "Osamu Oshiro and Kumi Kamada and Masataka Imura and
                 Kunihiro Chihara and Eiji Toyota and Yasuo Ogasawara
                 and Fumihiko Kajiya",
  title =        "Kidney Glomerulus Observation in Interactive {VR}
                 Space",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "629--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gunaratne:2003:EAF,
  author =       "Pujitha Gunaratne and Yukio Sato",
  title =        "Estimation of Asymmetry in Facial Actions for the
                 Analysis of Motion Dysfunction Due to Paralysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "639--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hussain:2003:FSF,
  author =       "Muhammad Hussain and Yoshihiro Okada and Koichi
                 Niijima",
  title =        "Fast, Simple, Feature Preserving and Memory Efficient
                 Simplification of Triangle Meshes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "653--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2003:AIV,
  author =       "Anonymous",
  title =        "Author Index Volume 3 (2003)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "3",
  number =       "4",
  pages =        "671--??",
  month =        oct,
  year =         "2003",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bennamoun:2004:E,
  author =       "Mohammed Bennamoun",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "1--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jan 27 07:06:41 MST 2004",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hu:2004:SCR,
  author =       "Zhencheng Hu and Keiichi Uchimura",
  title =        "Solution of camera registration problem via
                 {$3$D}--{$2$D} parameterized model matching for on-road
                 navigation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "3--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kanatani:2004:ATC,
  author =       "Kenichi Kanatani and Yasushi Kanazawa",
  title =        "Automatic thresholding for correspondence detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "21--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kanatani:2004:ADC,
  author =       "Kenichi Kanatani and Naoya Ohta",
  title =        "Automatic detection of circular objects by ellipse
                 growing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "35--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mamic:2004:ASS,
  author =       "G. Mamic and M. Bennamoun",
  title =        "Automated spline surface modeling and matching for
                 recognition of free-form objects",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "51--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhanu:2004:MLA,
  author =       "Bir Bhanu and Grinnell {Jones III}",
  title =        "Multiple look angle {SAR} recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "85--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Toulminet:2004:FAS,
  author =       "Gwena{\"e}lle Toulminet and St{\'e}phane Mousset and
                 Abdelaziz Bensrhair",
  title =        "Fast and accurate stereo vision-based estimation of
                 {$3$D} position and axial motion of road obstacles",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "99--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Roy:2004:MCU,
  author =       "Micha{\"e}l Roy and Sebti Foufou and Fr{\'e}d{\'e}ric
                 Truchetet",
  title =        "Mesh comparison using attribute deviation metric",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "1",
  pages =        "127--??",
  month =        jan,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:16 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Meegama:2004:FAP,
  author =       "Ravinda G. N. Meegama and Jagath C. Rajapakse",
  title =        "Fully Automated Peeling Technique for {T1}-Weighted,
                 High-Quality {MR} Head Scans",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "141--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 06 07:38:17 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lukac:2004:PBO,
  author =       "Rastislav Lukac",
  title =        "Performance Boundaries of Optimal Weighted Median
                 Filters",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "157--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gatos:2004:FIM,
  author =       "Basilios Gatos and Stavros J. Perantonis and Nikos
                 Papamarkos and Ioannis Andreadis",
  title =        "Fast Implementation of Morphological Operations Using
                 Binary Image Block Decomposition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "183--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Palacios:2004:HBC,
  author =       "Rafael Palacios and Amar Gupta and Patrick S. Wang",
  title =        "Handwritten Bank Check Recognition of Courtesy
                 Amounts",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "203--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sarkar:2004:GAB,
  author =       "Biswajit Sarkar and Lokendra Kumar Singh and Debranjan
                 Sarkar",
  title =        "A Genetic Algorithm-Based Approach for Detection of
                 Significant Vertices for Polygonal Approximation of
                 Digital Curves",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "223--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mari:2004:CFF,
  author =       "Jean-Luc Mari and Jean Sequeira",
  title =        "Closed Free-Form Surface Geometrical Modeling a New
                 Approach with Global and Local Characterization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "241--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Babu:2004:SGM,
  author =       "R. Venkatesh Babu and K. R. Ramakrishnan",
  title =        "Sprite Generation from {MPEG} Video Using Motion
                 Information",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "263--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Alhichri:2004:AIR,
  author =       "Haikel S. Alhichri and Mohamed Kamel",
  title =        "Automatic Image Registration Using Virtual Circles",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "281--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sarfraz:2004:SAC,
  author =       "Muhammad Sarfraz",
  title =        "Some Algorithms for Curve Design and Automatic Outline
                 Capturing of Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "301--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Koyama:2004:VPR,
  author =       "Kazuhiro Koyama and Yoshiaki Tomizawa and Minoru
                 Okada",
  title =        "Vectorization and Precise Refractions In Beam
                 Tracing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "2",
  pages =        "325--??",
  month =        apr,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2004:Ea,
  author =       "Anonymous",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "341--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Abd-Almageed:2004:ADM,
  author =       "Wael Abd-Almageed and Christopher E. Smith",
  title =        "Active Deformable Models Using Density Estimation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "343--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Giraldi:2004:ISI,
  author =       "Gilson A. Giraldi and Antonio A. F. Oliveira",
  title =        "Invariant Snakes and Initialization of Deformable
                 Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "363--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shao:2004:APS,
  author =       "Fan Shao and Keck Voon Ling and Wan Sing Ng",
  title =        "Automatic {$3$D} Prostate Surface Detection from
                 {TRUS} with Level Sets",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "385--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tohka:2004:DMA,
  author =       "Jussi Tohka and Jouni M. Mykk{\"a}nen",
  title =        "Deformable Mesh for Automated Surface Extraction from
                 Noisy Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "405--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pujol:2004:TSS,
  author =       "Oriol Pujol and Petia Radeva",
  title =        "Texture Segmentation By Statistical Deformable
                 Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "433--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yazdi:2004:IFP,
  author =       "Mehran Yazdi and Andre Zaccarin",
  title =        "Inter-Frame Prediction of Medical and Videophone
                 Sequences: a Deformable Triangle-Based Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "453--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tsechpenakis:2004:PBB,
  author =       "Gabriel Tsechpenakis and Nicolas Tsapatsoulis and
                 Stefanos Kollias",
  title =        "Probabilistic Boundary-Based Contour Tracking with
                 Snakes In Natural Cluttered Video Sequences",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "469--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dornaika:2004:FFF,
  author =       "F. Dornaika and J. Ahlberg",
  title =        "Face and Facial Feature Tracking Using Deformable
                 Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "3",
  pages =        "499--??",
  month =        jul,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2004:Eb,
  author =       "Anonymous",
  title =        "Editorial",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "533--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2004:DCI,
  author =       "Yongmei Michelle Wang and Jingdan Zhang and Zhunping
                 Zhang and Baining Guo",
  title =        "Directional Coherence Interpolation for
                 Three-Dimensional Gray-Level Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "535--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Theobalt:2004:CFT,
  author =       "Christian Theobalt and Marcus A. Magnor and Pascal
                 Sch{\"u}ler and Hans-Peter Seidel",
  title =        "Combining {$2$D} Feature Tracking and Volume
                 Reconstruction for Online Video-Based Human Motion
                 Capture",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "563--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Matsushita:2004:LSI,
  author =       "Yasuyuki Matsushita and Stephen Lin and Heung-Yeung
                 Shum and Xin Tong and Sing Bing Kang",
  title =        "Lighting and Shadow Interpolation Using Intrinsic
                 Lumigraphs",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "585--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yu:2004:SCS,
  author =       "Jingyi Yu and Leonard McMillan and Steven Gortler",
  title =        "Surface Camera ({SCAM}) Light Field Rendering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "605--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2004:UAR,
  author =       "Ruigang Yang and Marc Pollefeys and Hua Yang and Greg
                 Welch",
  title =        "A Unified Approach To Real-Time, Multi-Resolution,
                 Multi-Baseline {$2$D} View Synthesis and {$3$D} Depth
                 Estimation Using Commodity Graphics Hardware",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "627--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pajarola:2004:DFD,
  author =       "Renato Pajarola and Miguel Sainz and Yu Meng",
  title =        "{DMesh}: Fast Depth-Image Meshing and Warping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "653--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Brown:2004:IGR,
  author =       "Michael S. Brown and W. Brent Seales",
  title =        "Incorporating Geometric Registration with {PC}-Cluster
                 Rendering for Flexible Tiled Displays",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "683--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sun:2004:IBT,
  author =       "Jing Sun and George Baciu and Xiaobo Yu and Mark
                 Green",
  title =        "Image-Based Template Generation of Road Networks for
                 Virtual Maps",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "701--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2004:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 4)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "4",
  number =       "4",
  pages =        "721--??",
  month =        oct,
  year =         "2004",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 6 06:44:13 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2005:I,
  author =       "Anonymous",
  title =        "Introduction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "1--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Voloshynovskiy:2005:ITD,
  author =       "Sviatoslav Voloshynovskiy and Frederic Deguillaume and
                 Oleksiy Koval and Thierry Pun",
  title =        "Information-Theoretic Data-Hiding: Recent Achievements
                 and Open Problems",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "5--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lee:2005:IWR,
  author =       "Choong-Hoon Lee and Heung-Kyu Lee and Youngho Suh",
  title =        "Image Watermarking Resistant to Combined Geometric and
                 Removal Attacks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "37--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lu:2005:BIW,
  author =       "Haiping Lu and Yun Q. Shi and Alex C. Kot and Lihui
                 Chen",
  title =        "Binary Image Watermarking Through Blurring and Biased
                 Binarization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "67--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Echizen:2005:PAV,
  author =       "Isao Echizen and Yasuhiro Fujii and Takaaki Yamada and
                 Satoru Tezuka and Hiroshi Yoshiura",
  title =        "Perceptually Adaptive Video Watermarking Using Motion
                 Estimation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "89--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2005:DBV,
  author =       "Hongmei Liu and Jiwu Huang and Yun Q. Shi",
  title =        "{DWT}-Based Video Data Hiding Robust to {MPEG}
                 Compression and Frame Loss",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "111--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sun:2005:CSS,
  author =       "Qibin Sun and Shuiming Ye and Ching-Yung Lin and
                 Shih-Fu Chang",
  title =        "A Crypto Signature Scheme for Image Authentication
                 over Wireless Channel",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "135--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Piva:2005:SRA,
  author =       "Alessandro Piva and Franco Bartolini and Roberto
                 Caldelli",
  title =        "Self Recovery Authentication of Images in the {DWT}
                 Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "149--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sallee:2005:MBM,
  author =       "Phil Sallee",
  title =        "Model-Based Methods for Steganography and
                 Steganalysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "167--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gang:2005:CNI,
  author =       "Litao Gang and Ali N. Akansu",
  title =        "Cover Noise Interference Suppression in Multimedia
                 Data Hiding",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "1",
  pages =        "191--??",
  month =        jan,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 12 05:16:34 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cheriet:2005:SFB,
  author =       "Mohamed Cheriet and Jean-Christophe Demers and Sylvain
                 Deblois",
  title =        "Shock Filter-Based Diffusion Fields --- Application to
                 Grayscale Character Image Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "209--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Amin:2005:RST,
  author =       "Adnan Amin and Sue Wu",
  title =        "A Robust System for Thresholding and Skew Detection in
                 Mixed Text\slash Graphics Documents",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "247--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dawoud:2005:NSN,
  author =       "Amer Dawoud and Mohamed Kamel",
  title =        "Natural Skeletonization: New Approach for the
                 Skeletonization of Handwritten Characters",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "267--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chi:2005:DIB,
  author =       "Zheru Chi and Qing Wang",
  title =        "Document Image Binarization with Feedback for
                 Improving Character Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "281--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Brown:2005:DRC,
  author =       "Michael S. Brown and Yau-Chat Tsoi",
  title =        "Distortion Removal for Camera-Imaged Print Materials
                 Using Boundary Interpolation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "311--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lelandais:2005:STL,
  author =       "S. Lelandais and L. Boutte and J. Plantier",
  title =        "Shape from Texture: Local Scales and Vanishing Line
                 Computation to Improve Results for Macrotextures",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "329--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2005:AEM,
  author =       "Xiuying Wang and David Dagan Feng",
  title =        "Automatic Elastic Medical Image Registration Based On
                 Image Intensity",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "351--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhao:2005:HPR,
  author =       "Jianhui Zhao and Ling Li and Kwoh Chee Keong",
  title =        "Human Posture Reconstruction and Animation from
                 Monocular Images Based on Genetic Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "371--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bing:2005:EPR,
  author =       "Cheng Bing and Wang Ying and Zheng Nanning and Bian
                 Zhengzhong",
  title =        "An Efficient {$3$D} Plenoptic Representation for
                 Approximating a Path of Motion to a Curved Line",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "397--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2005:PVR,
  author =       "Wencheng Wang and Hanqiu Sun and Enhua Wu",
  title =        "Projective Volume Rendering by Excluding Occluded
                 Voxels",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "413--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kang:2005:NVE,
  author =       "Hyung W. Kang",
  title =        "Nonphotorealistic Virtual Environment Navigation From
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "2",
  pages =        "433--??",
  month =        apr,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Feb 7 16:17:59 MST 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ratschek:2005:SHM,
  author =       "Helmut Ratschek and Jon Rokne",
  title =        "{SCCI}-Hybrid Methods for {$2$D} Curve Tracing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "447--479",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001859",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001859.pdf",
  abstract =     "A hybrid method for plotting 2-dimensional curves,
                 defined implicitly by equations of the form f(x,y) = 0
                 is presented. The method is extremely robust and
                 reliable and consists of Space Covering techniques,
                 Continuation principles and Interval analysis (i.e.
                 SCCI). The space covering, based on iterated
                 subdivision, guarantees that no curve branches or
                 isolated curve parts or even points are lost (which can
                 happen if grid methods are used). The continuation
                 method is initiated in a subarea as soon as it is
                 proven that the subarea contains only one smooth curve.
                 Such a subarea does not need to be subdivided further
                 so that the computation is accelerated as far as
                 possible with respect to the subdivision process. The
                 novelty of the SCCI-hybrid method is the intense use of
                 the implicit function theorem for controlling the steps
                 of the method. Although the implicit function theorem
                 has a rather local nature, it is empowered with global
                 properties by evaluating it in an interval environment.
                 This means that the theorem can provide global
                 information about the curve in a subarea such as
                 existence, non-existence, uniqueness of the curve or
                 even the presence of singular points. The information
                 gained allows the above-mentioned control of the
                 subarea and the decision of its further processing,
                 i.e. deleting it, subdividing it, switching to the
                 continuation method or preparing the plotting of the
                 curve in this subarea. The curves can be processed
                 mathematically in such a manner, that the derivation of
                 the plotted curve from the exact curve is as small as
                 desired (modulo the screen resolution).",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  keywords =     "SCCI (Space Covering techniques, Continuation
                 principles and Interval analysis)",
}

@Article{Hicks:2005:AMC,
  author =       "B. J. Hicks and G. Mullineux and A. J. Medland",
  title =        "Automatic Model Creation for Kinematic Analysis and
                 Optimization of Engineering Systems",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "481--499",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001860",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001860.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hong:2005:RPE,
  author =       "Jin-Hyuk Hong and Eun-Kyung Yun and Sung-Bae Cho",
  title =        "A Review of Performance Evaluation for Biometrics
                 Systems",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "501--536",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001872",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001872.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhowmick:2005:DMS,
  author =       "Partha Bhowmick and Arijit Bishnu and Bhargab Bikram
                 Bhattacharya and Malay Kumar Kundu and C. A. Murthy and
                 Tinku Acharya",
  title =        "Determination of Minutiae Scores for Fingerprint Image
                 Applications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "537--571",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001896",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001896.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pan:2005:FRR,
  author =       "Gang Pan and Zhaohui Wu",
  title =        "{$3$D} Face Recognition from Range Data",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "573--593",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001884",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001884.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kharma:2005:PCC,
  author =       "Nawwaf Kharma and Ching Y. Suen and Pei F. Guo",
  title =        "{Palmprints}: a Cooperative Co-Evolutionary
                 Algorithm for Clustering Hand Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "595--616",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001902",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001902.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Drago:2005:LAC,
  author =       "Fr{\'e}d{\'e}ric Drago and Norishige Chiba",
  title =        "Locally Adaptive Chromatic Restoration of Digitally
                 Acquired Paintings",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "617--637",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001914",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001914.pdf",
  abstract =     "This article presents a semi-automatic procedure to
                 restore the visual appearance of aged paintings
                 converted to a digital form. The innovative
                 implementation of an image-processing algorithm based
                 on the Retinex theory of human vision alleviates layers
                 of yellowed varnish and dust, restores chromatic
                 balance and contrast, and recovers some of the original
                 painted details. This virtual cleaning of artwork is
                 totally non-intrusive and can be applied automatically
                 to color images of paintings or ancient illustrations.
                 Cleaned virtual reproductions help art historians and
                 restorers in their research and classification work,
                 and also show the artwork in good condition to a wide
                 audience while avoiding an always costly and dangerous
                 manual restoration.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Noyer:2005:SME,
  author =       "Jean-Charles Noyer and Christophe Boucher and Mohammed
                 Benjelloun",
  title =        "{$3$D} Structure and Motion Estimation from Range and
                 Intensity Images Using Particle Filtering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "639--661",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001926",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001926.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Taylor-Hell:2005:SAR,
  author =       "Julia F. Taylor-Hell and Gladimir V. G. Baranoski and
                 Jon G. Rokne",
  title =        "State of the Art in the Realistic Modeling of Plant
                 Venation Systems",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "663--678",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467805001938",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S0219467805001938.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Marchesotti:2005:VPU,
  author =       "Luca Marchesotti and Carlo Regazzoni and Carlo
                 Bonamico and Fabio Lavagetto",
  title =        "Video Processing and Understanding Tools for Augmented
                 Multisensor Perception and Mobile User Interaction in
                 Smart Spaces",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "3",
  pages =        "679--698",
  month =        jul,
  year =         "2005",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780500194X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 5 06:13:03 MDT 2005",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "http://ejournals.wspc.com.sg/ijig/05/preserved-docs/0503/S021946780500194X.pdf",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhou:2005:COV,
  author =       "Jianlong Zhou and Andreas D{\"o}ring and Klaus D.
                 T{\"o}nnies",
  title =        "Control of Object Visibility in Volume Rendering ---
                 a Distance-Based Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "699--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2005:NMA,
  author =       "Qiang Wang and Hongbo Chen and Xiaorong Xu and Haiyan
                 Liu",
  title =        "A Newly Modified Algorithm of {Hough Transform} for
                 Line Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "715--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhu:2005:FMR,
  author =       "En Zhu and Jian-Ping Yin and Guo-Min Zhang and
                 Chun-Feng Hu",
  title =        "Fingerprint Minutiae Relationship Representation and
                 Matching Based on Curve Coordinate System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "729--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hu:2005:LBR,
  author =       "Yu-Chen Hu",
  title =        "Low Bit-Rate Image Compression Schemes Based on Vector
                 Quantization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "745--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2005:MSM,
  author =       "Yong-Jin Liu and Kai Tang and Ajay Joneja and Matthew
                 Ming-Fai Yuen",
  title =        "Multiresolution Shape Modeling and Editing in Reverse
                 Engineering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "765--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2005:FHS,
  author =       "Chandan Singh and Ekta Walia",
  title =        "Fast Hybrid Shading: an Application of Finite Element
                 Methods in {$3$D} Rendering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "789--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rahman:2005:ODE,
  author =       "M. Masudur Rahman and Seiji Ishikawa",
  title =        "Overcoming Dress Effect in Eigenspace",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "811--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rahman:2005:HPR,
  author =       "M. Masudur Rahman and Seiji Ishikawa",
  title =        "Human Posture Recognition: Eigenspace Tuning by a Mean
                 Eigenspace",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "825--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chen:2005:TSC,
  author =       "Zhe Chen and David Dagan Feng and Weidong Cai",
  title =        "Temporal and Spatial Compression of Dynamic Positron
                 Emission Tomography in Sinogram Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "839--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Deng:2005:PBS,
  author =       "Yuhui Deng and Frank Wang and Jiangling Zhang and Dan
                 Feng and Fang Wang and Hong Jiang",
  title =        "Push the Bottleneck of Streaming Media System from
                 Streaming Media Server to Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "859--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2005:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 5)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "5",
  number =       "4",
  pages =        "871--??",
  month =        oct,
  year =         "2005",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kim:2006:VPC,
  author =       "Jaeho Kim and Hyungseok Kim and Kwangyun Wohn",
  title =        "Visibility Preprocessing for Complex {$3$D} Scenes
                 Using Hardware-Visibility Queries",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "1--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Xu:2006:NCW,
  author =       "Qing Xu and Wei Wang and Shiqiang Bao",
  title =        "A New Computational Way to {Monte Carlo} Global
                 Illumination",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "23--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2006:PMW,
  author =       "Li Li and Zhigeng Pan and David Zhang",
  title =        "A Public Mesh Watermarking Algorithm Based on Addition
                 Property of {Fourier Transform}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "35--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cao:2006:DRD,
  author =       "Weiqun Cao and Hendrik Gaertner and Hannes Guddat and
                 Andreas M. Straube and Stefan Conrad and Ernst Kruijff
                 and Dirk Langenberg",
  title =        "Design Review in a Distributed Collaborative Virtual
                 Environment",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "45--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tan:2006:IVE,
  author =       "Jiacheng Tan and Gordon J. Clapworthy and Igor R.
                 Belousov",
  title =        "The Integration of a Virtual Environment and {$3$D}
                 Modeling Tools in a Networked Robot System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "65--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Favier:2006:DAI,
  author =       "Pierre-Alexandre Favier and Pierre {De Loor}",
  title =        "From Decision to Action: Intentionality, a Guide for
                 the Specification of Intelligent Agent's Behavior",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "87--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2006:IST,
  author =       "Ajay Kumar and David Zhang",
  title =        "Integrating Shape and Texture for Hand Verification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "101--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zheng:2006:SBA,
  author =       "Qing-Fang Zheng and Wei Zeng and Wei-Qiang Wang and
                 Wen Gao",
  title =        "Shape-Based Adult Image Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "115--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhan:2006:FER,
  author =       "Yongzhao Zhan and Jingfu Ye and Dejiao Niu and Peng
                 Cao",
  title =        "Facial Expression Recognition Based on {Gabor} Wavelet
                 Transformation and Elastic Templates Matching",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "125--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2006:AMS,
  author =       "Xuelong Li and Yuan Yuan and Dacheng Tao",
  title =        "Artistic Mosaic Series Generation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "1",
  pages =        "139--??",
  month =        jan,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2006:DLL,
  author =       "Zhenlan Wang and Chee-Kong Chui and Yiyu Cai and
                 Chuan-Heng Ang and Swee-Hin Teoh",
  title =        "Dynamic Linear Level Octree-Based Volume Rendering
                 Methods for Interactive Microsurgical Simulation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "155--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fang:2006:MSI,
  author =       "Shiaofen Fang and Marwan Adada",
  title =        "Multi-Scale Iso-Surface Extraction for Volume
                 Visualization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "173--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lakshmipathy:2006:TBI,
  author =       "Jagannathan Lakshmipathy and Wieslaw L. Nowinski and
                 Eric A. Wernert",
  title =        "Template-Based Isocontouring",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "187--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Heng:2006:HNM,
  author =       "Pheng-Ann Heng and Tien-Tsin Wong and Ka-Man Leung and
                 Yim-Pan Chui and Hanqiu Sun",
  title =        "A Haptic Needle Manipulation Simulator for {Chinese}
                 Acupuncture Learning and Training",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "205--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Guan:2006:VEC,
  author =       "Y. Q. Guan and Y. Y. Cai and M. Opas and Z. W. Xiong
                 and Y. T. Lee",
  title =        "A {VR} Enhanced Collaborative System for {$3$D}
                 Confocal Microscopic Image Processing and
                 Visualization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "231--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lu:2006:BNS,
  author =       "Baifang Lu and Zhaowei Fan and Jianmin Zheng and Lin
                 Li",
  title =        "Bio-Native Shape Modeling and Virtual Reality for Bio
                 Education",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "251--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Djemal:2006:AAC,
  author =       "Khalifa Djemal and William Puech and Bruno Rossetto",
  title =        "Automatic Active Contours Propagation in a Sequence of
                 Medical Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "267--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Marcialis:2006:DLF,
  author =       "Gian Luca Marcialis and Fabio Roli",
  title =        "Decision-Level Fusion of {PCA} and {LDA}-Based Face
                 Recognition Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "2",
  pages =        "293--??",
  month =        apr,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gong:2006:RTI,
  author =       "Minglun Gong and Yee-Hong Yang",
  title =        "{Rayset}: a Taxonomy for Image-Based Rendering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "313--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Knopf:2006:FSR,
  author =       "George K. Knopf and Archana P. Sangole",
  title =        "Freeform Surface Reconstruction from Scattered Points
                 Using a Deformable Spherical Map",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "341--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{AlAghbari:2006:RBS,
  author =       "Zaher {Al Aghbari}",
  title =        "Region-Based Semantic Image Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "357--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tizhoosh:2006:RCA,
  author =       "Hamid R. Tizhoosh and Graham W. Taylor",
  title =        "Reinforced Contrast Adaptation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "377--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhou:2006:SAR,
  author =       "Hong Zhou and Ray Seyfarth",
  title =        "Semi Automatic Registration of Partially Overlapped
                 Aerial Images Via Pattern Search Method",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "393--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2006:OSV,
  author =       "Bin Li and David Zhang and Kuanquan Wang",
  title =        "Online Signature Verification by Combining Shape
                 Contexts and Local Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "407--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{ElRube:2006:MRS,
  author =       "Ibrahim {El Rub{\'e}} and Naif Alajlan and Mohamed S.
                 Kamel and Maher Ahmed and George H. Freeman",
  title =        "{Mtar}: a Robust {$2$D} Shape Representation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "421--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhong:2006:HDS,
  author =       "Yongmin Zhong and Bijan Shirinzadeh and Gursel Alici
                 and Julian Smith",
  title =        "Haptic Deformation Simulation with {Poisson}
                 Equation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "445--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bao:2006:RTS,
  author =       "Paul Bao and Xiaohu Ma and Wan-Chi Siu",
  title =        "Real-Time Seamless Texture Synthesis Based on Patch
                 Quantization Clustering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "475--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fang:2006:NMF,
  author =       "Xianyong Fang and Zhigeng Pan and Gaoqi He and Li
                 Li",
  title =        "A New Method of Feature Based Image Mosaic",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "3",
  pages =        "497--??",
  month =        jul,
  year =         "2006",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 23 08:55:54 MDT 2006",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ng:2006:IFN,
  author =       "Geok See Ng and Sevki Erdogan and Daming Shi and Abdul
                 Wahab",
  title =        "Insight of Fuzzy Neural Systems in the Application of
                 Handwritten Digits Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "511--532",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002410",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cai:2006:DIW,
  author =       "Weiting Cai and Malek Adjouadi",
  title =        "Design and Implementation of Wavelet-Domain Video
                 Compression Using Multiresolution Motion Estimation and
                 Compensation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "533--549",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002471",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Faudot:2006:SVV,
  author =       "Dominique Faudot and Gilles Gesquiere",
  title =        "Study of Volume Variation of Implicit Objects",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "551--568",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002483",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Regentova:2006:ISU,
  author =       "Emma Regentova and Dongsheng Yao and Shahram Latifi
                 and Jun Zheng",
  title =        "Image Segmentation Using Ncut in the Wavelet Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "569--582",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002458",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yu:2006:CIR,
  author =       "Shengsheng Yu and Chaobing Huang and Jingli Zhou",
  title =        "Color Image Retrieval Based on Color-Texture-Edge
                 Feature Histograms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "583--598",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002392",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chow:2006:FRR,
  author =       "S. K. Chow and K. L. Chan",
  title =        "Fast and Realistic Rendering of Deformable Virtual
                 Characters Using Impostor and Stencil Buffer",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "599--624",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002409",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Skala:2006:LAV,
  author =       "Vaclav Skala",
  title =        "Length, Area and Volume Computation in Homogeneous
                 Coordinates",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "625--639",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002422",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2006:RTM,
  author =       "Xiaoying Li and Enhua Wu",
  title =        "Relief Texture Mapping on Field Programmable Gate
                 Array",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "641--655",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780600246X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hardy:2006:HII,
  author =       "Alexandre Hardy and Willi-Hans Steeb",
  title =        "Harmonic Interpolation for Image Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "657--675",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002434",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bi:2006:NNR,
  author =       "Dong-Liang Bi and Wei Guo and Ai-Dong Xu",
  title =        "A New Noise Removing Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "677--687",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002446",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2006:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 6)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "6",
  number =       "4",
  pages =        "689--691",
  month =        oct,
  year =         "2006",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467806002446",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liang:2007:GEM,
  author =       "Jerome Zhengrong Liang and Hongbing Lu and Dimitris N.
                 Metaxas and Joseph M. Reinhardt",
  title =        "Guest Editorial: Medical Imaging Informatics --- An
                 Information Processing from Image Formation to
                 Visualization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "1--15",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002568",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hawkins:2007:ESU,
  author =       "William G. Hawkins",
  title =        "On the Equivalence of Stable and Unstable Forms of the
                 Inverse Circular Harmonic Transform Solution for the
                 {Radon} Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "17--33",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002519",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kao:2007:EAA,
  author =       "Chien-Min Kao and Yu Zou and Seungryong Cho and
                 Xiaochuan Pan",
  title =        "An Exact Analytic Approach to {$3$D} {PET} Image
                 Reconstruction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "35--54",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002520",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gao:2007:FVO,
  author =       "Xin Gao and Yuanmei Wang and Cishen Zhang",
  title =        "Fuzzy Vector Objective Optimization Algorithm for
                 Image Reconstruction from Incomplete Projections",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "55--69",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002532",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Huang:2007:AIE,
  author =       "Qiu Huang and Gengsheng L. Zeng and Grant T.
                 Gullberg",
  title =        "An Analytical Inversion of the $180^\circ$ Exponential
                 {Radon} Transform With a Numerically Generated Kernel",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "71--85",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002544",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fan:2007:FAR,
  author =       "Yi Fan and Hongbing Lu and Chongyang Hao and Zhengrong
                 Liang and Zhiming Zhou",
  title =        "Fast Analytical Reconstruction of Gated Cardiac
                 {SPECT} with Non-Uniform Attenuation Compensation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "87--104",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002556",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2007:ISE,
  author =       "Zhenguo Wang and Christopher S. D. Lee and Wayne C.
                 Waltzer and Zhijia Yuan and Yingtian Pan",
  title =        "Interpixel-Shifted Endoscopic Optical Coherence
                 Tomography for in Vivo Bladder Cancer Diagnosis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "105--117",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780700257X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lei:2007:MRM,
  author =       "Tianhu Lei and Felix W. Wehrli",
  title =        "Magnetic Resonance ({MR}) Image Analysis --- a
                 Statistical Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "119--141",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002581",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cui:2007:DMF,
  author =       "Yunfeng Cui and Jing Bai and Yingmao Chen and Jiahe
                 Tian",
  title =        "A Digital Model Framework of Metabolic System Based on
                 Visible Human Data Set",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "143--157",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002593",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cai:2007:CVC,
  author =       "Wenli Cai and Gordon J. Harris and Hiroyuki Yoshida",
  title =        "Computation of Vesselness in {CTA} Images for Fast and
                 Interactive Vessel Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "159--176",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780700260X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Papaleo:2007:ASR,
  author =       "Laura Papaleo",
  title =        "An Approach to Surface Reconstruction Using Uncertain
                 Data",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "177--194",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002611",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tian:2007:DEV,
  author =       "Yun Tian and Chongyang Hao and Yi Wang and Guiqing He
                 and Jun Wei and Haitiao Zhao and Benhua Zhao",
  title =        "Dynamic Extraction for {VOI} from {CT} Images Based on
                 Volume Rendering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "1",
  pages =        "195--209",
  month =        jan,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002623",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2007:SMI,
  author =       "Xuelong Li and Jing Li and Dacheng Tao and Yuan
                 Yuan",
  title =        "A Similarity Metric in Image Searching",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "211--225",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002635",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tao:2007:RCO,
  author =       "Ji Tao and Yap-Peng Tan and Wenmiao Lu",
  title =        "Robust Color Object Tracking with Application to
                 People Monitoring",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "227--254",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002647",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Werghi:2007:LTD,
  author =       "Naoufel Werghi and Yijun Xiao and Paul Siebert",
  title =        "Labelling of Three Dimensional Human Body Scans: a
                 Topological Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "255--272",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002659",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Livny:2007:DAP,
  author =       "Yotam Livny and Neta Sokolovsky and Jihad El-Sana",
  title =        "Dual Adaptive Paths for Multiresolution Hierarchies",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "273--290",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002726",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Abdelwahab:2007:FCD,
  author =       "Ahmed A. Abdelwahab and Nora S. Muharram",
  title =        "A Fast Codebook Design Algorithm Based on a Fuzzy
                 Clustering Methodology",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "291--302",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002714",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ayed:2007:TOB,
  author =       "Mohamed Ali Ben Ayed and Amine Samet and Nouri
                 Masmoudi",
  title =        "Toward an Optimal Block Motion Estimation Algorithm
                 for {H.264\slash AVC}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "303--320",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002660",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Djebali:2007:CBM,
  author =       "M. Djebali and M. Melkemi and K. Melkemi and N.
                 Sapidis",
  title =        "Coiflet Based Methods for Range Image Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "321--351",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002672",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2007:ADM,
  author =       "Liang Zhang and Qingping Lin and Robert Gay and
                 Guangbin Huang and Norman Neo",
  title =        "An Autonomous Decentralized Multi-Server Framework for
                 Large Scale Collaborative Virtual Environments",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "353--375",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002684",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nagendraswamy:2007:NMR,
  author =       "H. S. Nagendraswamy and D. S. Guru",
  title =        "A New Method of Representing and Matching Two
                 Dimensional Shapes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "377--405",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002696",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Verma:2007:CSI,
  author =       "Nishchal K. Verma and M. Hanmandlu",
  title =        "Color Segmentation Via Improved Mountain Clustering
                 Technique",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "2",
  pages =        "407--426",
  month =        apr,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002702",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Xu:2007:MCM,
  author =       "Xinyu Xu and Baoxin Li",
  title =        "Multiple Class Multiple-Instance Learning and Its
                 Application to Image Categorization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "427--444",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780700274X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cheng:2007:OBP,
  author =       "Jun Cheng and Ronald Chung and Edmund Y. Lam and
                 Kenneth S. M. Fung and Yangsheng Xu",
  title =        "Optimization of Bit-Pairing Codification with Learning
                 for {$3$D} Reconstruction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "445--462",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002763",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Song:2007:LLD,
  author =       "Mingli Song and Huiqiong Wang and Chun Chen",
  title =        "Local {Laplacian} Detail Learning for Face Aging
                 Manipulation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "463--480",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002775",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2007:NBA,
  author =       "Weihai Li and Yuan Yuan",
  title =        "A New Blind Attack Procedure for {DCT}-Based Image
                 Encryption with Spectrum Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "481--496",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002787",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhou:2007:IRD,
  author =       "Huiyu Zhou and Tangwei Liu and Faquan Lin and Yusheng
                 Pang and Ji Wu",
  title =        "Image Restoration and Detail Preservation by
                 {Bayesian} Estimation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "497--514",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002738",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2007:TDI,
  author =       "Chunsheng Liu and Tianxu Zhang and Biyin Zhang",
  title =        "Turbulence Degraded Images Restoration Based on
                 Improved Multiframe Iterative Loops and Data Mining",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "515--527",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002799",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2007:BCR,
  author =       "Kongqiao Wang and Yanming Zou and Hao Wang",
  title =        "{$1$D} Bar Code Reading on Camera Phones",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "529--550",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002805",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shen:2007:ESQ,
  author =       "Jialie Shen and John Shepherd and Anne H. H. Ngu",
  title =        "An Empirical Study of Query Effectiveness Improvement
                 Via Multiple Visual Feature Integration",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "551--581",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002751",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Xu:2007:FRG,
  author =       "Dong Xu and Dacheng Tao and Xuelong Li and Shuicheng
                 Yan",
  title =        "Face Recognition --- a Generalized Marginal {Fisher}
                 Analysis Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "583--591",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002817",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2007:MLI,
  author =       "Anonymous",
  title =        "Machine Learning in Image and Graphics",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "3",
  pages =        "v--v",
  month =        jul,
  year =         "2007",
  CODEN =        "????",
  DOI =          "",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Brunn:2007:CRU,
  author =       "Meru Brunn and Mario Costa Sousa and Faramarz F.
                 Samavati",
  title =        "Capturing and Re-Using Artistic Styles with Reverse
                 Subdivision-Based Multiresolution Methods",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "593--615",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002829",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Curran:2007:APD,
  author =       "Kevin Curran and Neil McCaughley and Xuelong Li",
  title =        "Addressing the Problems of Detecting Faces with Neural
                 Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "617--640",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002830",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Park:2007:FMO,
  author =       "Chan Jong Park and Kwang Yun Wohn",
  title =        "Fusion of the Magnetic and Optical Information for
                 Motion Capturing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "641--662",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002842",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Khare:2007:DCW,
  author =       "Ashish Khare and Uma Shanker Tiwary",
  title =        "{Daubechies} Complex Wavelet Transform Based Technique
                 for Denoising of Medical Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "663--687",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002854",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bougleux:2007:SDS,
  author =       "S{\'e}bastien Bougleux and Mahmoud Melkemi and
                 Abderrahim Elmoataz",
  title =        "Structure Detection from a {$3$D} Set of Points with
                 Anisotropic Alpha-Shapes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "689--708",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002866",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ablameyko:2007:RED,
  author =       "Sergey V. Ablameyko and Seiichi Uchida",
  title =        "Recognition of Engineering Drawing Entities: Review of
                 Approaches",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "709--733",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002878",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hosny:2007:ECL,
  author =       "Khalid M. Hosny",
  title =        "Efficient Computation of {Legendre} Moments for Gray
                 Level Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "735--747",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780700288X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sanchez:2007:CFT,
  author =       "Danmary Sanchez and Malek Adjouadi and Nolan R. Altman
                 and Daniel Sanchez and Byron Bernal",
  title =        "Comprehensive {$3$D} Fiber Tracking As a New
                 Visualization System in Brain Studies",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "749--765",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002891",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2007:KGR,
  author =       "Jing Li and Yuan Yuan",
  title =        "Kernel {GBDA} for Relevance Feedback in Image
                 Retrieval",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "767--776",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467807002908",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2007:LGB,
  author =       "Wenchao Zhang and Shiguang Shan and Xilin Chen and Wen
                 Gao",
  title =        "Local {Gabor} Binary Patterns Based on Mutual
                 Information for Face Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "777--793",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780700291X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2007:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 7)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "7",
  number =       "4",
  pages =        "795--797",
  month =        oct,
  year =         "2007",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780700291X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Feng:2008:TFM,
  author =       "Guiyu Feng and David Zhang and Jian Yang and Dewen
                 Hu",
  title =        "A Theoretical Framework for Matrix-Based Feature
                 Extraction Algorithms with Its Application to Image
                 Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "1--23",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808002940",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lu:2008:NRM,
  author =       "Jianming Lu and Ling Wang and Yeqiu Li and Takashi
                 Yahagi",
  title =        "Noise Removal for Medical {X}-Ray Images in
                 Multiwavelet Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "25--46",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808002952",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Manjunath:2008:DSD,
  author =       "A. V. N. Manjunath and K. G. Hemantha and S.
                 Noushath",
  title =        "Document Skew Detection --- a Novel Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "47--59",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808002964",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2008:VBT,
  author =       "Shiueng-Bien Yang",
  title =        "Variable-Branch Tree-Structured Residual Vector
                 Quantization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "61--80",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808002976",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Courty:2008:ANR,
  author =       "Nicolas Courty and Pierre Hellier",
  title =        "Accelerating {$3$D} Non-Rigid Registration Using
                 Graphics Hardware",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "81--98",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808002988",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zou:2008:RCM,
  author =       "Jie Zou",
  title =        "Rose Curve Model and an Analytical Solution for
                 Estimating Its Parameters",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "99--108",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780800299X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ho:2008:RSI,
  author =       "Charlotte Yuk-Fan Ho and Tai-Chiu Hsung and Daniel
                 Pak-Kong Lun and Bingo Wing-Kuen Ling and Peter
                 Kwong-Shun Tam and Wan-Chi Siu",
  title =        "Regularity Scalable Image Coding Based on Wavelet
                 Singularity Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "109--134",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003003",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fabrizio:2008:ASP,
  author =       "Jonathan Fabrizio and Jean Devars",
  title =        "An Analytical Solution to the Perspective-{$n$}-Point
                 Problem for Common Planar Camera and for Catadioptric
                 Sensor",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "135--155",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003015",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chang:2008:LFE,
  author =       "Kuan-Tsung Chang and Tian-Yuan Shih",
  title =        "Linear Features Extraction with an Orientation
                 Constrained Probabilistic {Hough} Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "157--168",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003027",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Didier:2008:HCP,
  author =       "Jean-Yves Didier and Fakhr-Eddine Ababsa and Malik
                 Mallem",
  title =        "Hybrid Camera Pose Estimation Combining Square
                 Fiducials Localization Technique and Orthogonal
                 Iteration Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "1",
  pages =        "169--188",
  month =        jan,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003039",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:01 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ge:2008:PBA,
  author =       "Jinghua Ge and Daniel J. Sandin and Tom Peterka and
                 Robert Kooima and Javier I. Girado and Andrew Johnson",
  title =        "A Point-Based Asynchronous Remote Visualization
                 Framework for Real-Time Virtual Reality",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "189--207",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003040",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Peng:2008:RNP,
  author =       "Haoyu Peng and Hua Xiong and Zhen Liu and Jiaoying
                 Shi",
  title =        "Research of Nested Parallel Pipelines on Parallel
                 Graphics Rendering System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "209--222",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003052",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2008:HAP,
  author =       "Fan Zhang and Hanqiu Sun and Leilei Xu and Kitlun
                 Lee",
  title =        "Hardware-Accelerated Parallel-Split Shadow Maps",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "223--241",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003064",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ahlborn:2008:DIF,
  author =       "Benjamin A. Ahlborn and Oliver Kreylos and Sohail
                 Shafii and Bernd Hamann and Oliver G. Staadt",
  title =        "Design and Implementation of a Foveal Projection
                 Display",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "243--263",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003076",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhong:2008:RDB,
  author =       "Yongmin Zhong and Bijan Shirinzadeh and Julian
                 Smith",
  title =        "Reaction-Diffusion Based Deformable Object
                 Simulation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "265--280",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003088",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Baciu:2008:GSW,
  author =       "George Baciu and Liang Ma and Jinlian Hu",
  title =        "Generating Seams and Wrinkles for Virtual Clothing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "281--297",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780800309X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Scherff:2008:IPI,
  author =       "Phillip-Christoph Scherff and George Baciu and Jinlian
                 Hu",
  title =        "Intuitive Parameterized Input Interface for
                 Proportional Reshaping of Human Bodies",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "299--325",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003106",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2008:P,
  author =       "Anonymous",
  title =        "Preface",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "2",
  pages =        "vii--vii",
  month =        apr,
  year =         "2008",
  CODEN =        "????",
  DOI =          "",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Leung:2008:UID,
  author =       "Man-Kang Leung and Chi-Wing Fu",
  title =        "A User Interface Design for Acquiring Statistics from
                 Video",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "327--349",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780800312X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Agarwal:2008:DWS,
  author =       "Rashmi Agarwal and M. S. Santhanam",
  title =        "Digital Watermarking in the Singular Vector Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "351--368",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003131",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2008:VTA,
  author =       "Tao Yang and Jing Li and Quan Pan and Yong-Mei
                 Cheng",
  title =        "Visual Tracking with Automatic Confident Region
                 Extraction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "369--381",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003143",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chan:2008:VBG,
  author =       "K. L. Chan",
  title =        "Video-Based Gait Analysis by Silhouette {Chamfer}
                 Distance and {Kalman} Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "383--418",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003155",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yau:2008:VSR,
  author =       "Wai Chee Yau and Dinesh Kant Kumar and Sridhar
                 Poosapadi Arjunan",
  title =        "Visual Speech Recognition Using Dynamic Features and
                 Support Vector Machines",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "419--437",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003167",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sun:2008:BWN,
  author =       "Shusen Sun and Zhigeng Pan and Tae-Wan Kim",
  title =        "Blind Watermarking of Non-Uniform {B}-Spline
                 Surfaces",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "439--454",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003179",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Snidaro:2008:THM,
  author =       "Lauro Snidaro and Gian Luca Foresti and Luca
                 Chittaro",
  title =        "Tracking Human Motion from Monocular Sequences",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "455--471",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003180",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lezoray:2008:GBO,
  author =       "O. Lezoray and C. Meurie and A. Elmoataz",
  title =        "Graph-Based Ordering Scheme for Color Image
                 Filtering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "3",
  pages =        "473--493",
  month =        jul,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003192",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Coli:2008:FSR,
  author =       "Pietro Coli and Gian Luca Marcialis and Fabio Roli",
  title =        "Fingerprint Silicon Replicas: Static and Dynamic
                 Features for Vitality Detection Using an Optical
                 Capture Device",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "495--512",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003209",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hussain:2008:WBE,
  author =       "Muhammad Hussain and Turghunjan Abdukirim and
                 Yoshihiro Okada",
  title =        "Wavelet-Based Edge Detection in Digital Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "513--533",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003210",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhu:2008:MSS,
  author =       "Dengming Zhu and Zhaoqi Wang and Yingping Zhang",
  title =        "Motion Synthesis from the Semantic Signals",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "535--550",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003222",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kampke:2008:AGR,
  author =       "Thomas K{\"a}mpke",
  title =        "Automatic Generation of {$3$D} Radar Display Views",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "551--572",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003234",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Thakoor:2008:AVO,
  author =       "Ninad Thakoor and Jean X. Gao",
  title =        "Automatic Video Object Extraction with Camera in
                 Motion",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "573--600",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003246",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Xin:2008:IST,
  author =       "Binjie Xin and Jinlian Hu and George Baciu",
  title =        "An Imaging System for Textile Surface Profile Based on
                 Silhouette Image Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "601--613",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003258",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Skala:2008:ICP,
  author =       "Vaclav Skala",
  title =        "Intersection Computation in Projective Space Using
                 Homogeneous Coordinates",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "615--628",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780800326X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ou:2008:LIT,
  author =       "Chien-Min Ou and Hui-Ya Li and Wen-Jyi Hwang and
                 Mei-Hwa Liu",
  title =        "Layered Image Transmission with Quality
                 Pre-Specifiable {JPEG2000}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "629--641",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003271",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2008:NAS,
  author =       "Jing Li and Tao Yang and Quan Pan and Yong-Mei Cheng
                 and Jun Hou",
  title =        "A Novel Algorithm for Speeding Up Keypoint Detection
                 and Matching",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "643--661",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003283",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2008:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 8)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "8",
  number =       "4",
  pages =        "663--665",
  month =        oct,
  year =         "2008",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467808003283",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lao:2009:ORA,
  author =       "Yuanwei Lao and Yuan F. Zheng",
  title =        "Optimal Rate Allocation for Logo Watermarking",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "1--25",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003319",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Porta:2009:NVM,
  author =       "Marco Porta",
  title =        "New Visualization Modes for Effective Image
                 Presentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "27--49",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003320",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chen:2009:IRB,
  author =       "Huawei Chen and Ichiro Hagiwara and A. Kiet Tieu",
  title =        "Image Reconstruction Based on Combination of Wavelet
                 Decomposition, Inpainting and Texture Synthesis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "51--65",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003332",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Vasuki:2009:EAC,
  author =       "S. Vasuki and L. Ganesan",
  title =        "An Efficient Approach to Color Image Segmentation
                 Using Intermediate Features of Maximum Overlap Wavelet
                 Transform in Peak Finding Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "67--76",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003344",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arivazhagan:2009:TCU,
  author =       "S. Arivazhagan and L. Ganesan",
  title =        "Texture Characterization Using {WSFS} and {WCFS}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "77--100",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003356",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Miyamoto:2009:FAM,
  author =       "Kentaro Miyamoto and Tetsuo Kamina and Tetsuo Sugiyama
                 and Keisuke Kameyama and Kazuo Toraichi and Yasuhiro
                 Ohmiya",
  title =        "A Function Approximation Method for Images with
                 Grading Regions",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "101--119",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003307",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zongqing:2009:NFB,
  author =       "Lu Zongqing and Liao Qingmin and Pei Jihong",
  title =        "A Nonlinear Filtering Based Optical Flow Computation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "121--132",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003368",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ng:2009:RFM,
  author =       "Ada N. Y. Ng and Edmund Y. Lam and Ronald Chung and
                 Kenneth S. M. Fung and W. H. Leung",
  title =        "Reference-Free Machine Vision Inspection of
                 Semiconductor Die Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "1",
  pages =        "133--152",
  month =        jan,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780900337X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2009:JSN,
  author =       "Xingyuan Wang and Wenjing Song and Lixian Zou",
  title =        "{Julia} Set of the {Newton} Method for Solving Some
                 Complex Exponential Equation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "153--169",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003381",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nikam:2009:COP,
  author =       "Shankar Bhausaheb Nikam and Suneeta Agarwal",
  title =        "Co-Occurrence Probabilities and Wavelet-Based Spoof
                 Fingerprint Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "171--199",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003393",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2009:GOM,
  author =       "Shixue Zhang and Enhua Wu",
  title =        "Generation of Optimal Multiresolution Models for
                 Deforming Mesh Sequence",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "201--215",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780900340X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Stylianou:2009:IBF,
  author =       "Georgios Stylianou and Andreas Lanitis",
  title =        "Image Based {$3$D} Face Reconstruction: a Survey",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "217--250",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003411",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2009:UAU,
  author =       "Ajay Kumar and David Zhang",
  title =        "User Authentication Using Fusion of Face and
                 Palmprint",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "251--270",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003423",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Goh:2009:TSI,
  author =       "Hock-Ann Goh and Chee-Way Chong and Rosli Besar and
                 Fazly Salleh Abas and Kok-Swee Sim",
  title =        "Translation and Scale Invariants of {Hahn} Moments",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "271--285",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003435",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Aiger:2009:GBA,
  author =       "Dror Aiger and Klara Kedem",
  title =        "A {GPU}-Based Algorithm for Approximately Finding the
                 Largest Common Point Set in the Plane Under Similarity
                 Transformation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "287--298",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003459",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2009:NWM,
  author =       "Vipula Singh and Navin Rajpal and K. Srikanta
                 Murthy",
  title =        "A Neuro-Wavelet Model Using Fuzzy Vector Quantization
                 for Efficient Image Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "2",
  pages =        "299--320",
  month =        apr,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003447",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jindal:2009:STC,
  author =       "Manish Kumar Jindal and Gurpreet Singh Lehal and
                 Rajendra Kumar Sharma",
  title =        "On Segmentation of Touching Characters and Overlapping
                 Lines in Degraded Printed {Gurmukhi} Script",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "321--353",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003460",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gao:2009:FRS,
  author =       "Xinbo Gao and Jinxiu Li and Bing Xiao",
  title =        "A Face Recognition Scheme Based on Embedded Hidden
                 {Markov} Model and Selective Ensemble Strategy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "355--367",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003472",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Maurer:2009:PVL,
  author =       "Mauricio Rafael Maurer and Helio Pedrini and Marco
                 Antonio Ferreira Randi",
  title =        "Processing and Visualization of Light Microscope
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "369--388",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003484",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kamath:2009:ICS,
  author =       "Chandrika Kamath and Abel Gezahegne and Paul Miller",
  title =        "Identification of Coherent Structures in
                 Three-Dimensional Simulations of a Fluid-Mix Problem",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "389--410",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003502",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Seddik:2009:IWB,
  author =       "Hassen Seddik and Mounir Sayadi and Farhat Fnaiech and
                 Mohamed Cheriet",
  title =        "Image Watermarking Based on the {Hessenberg}
                 Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "411--433",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003514",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2009:DEO,
  author =       "Gaobo Yang and Weiwei Chen and Xiao Jing Wang and
                 Zhaoyang Zhang",
  title =        "Dense Estimation of Optical Flow Field Within the
                 {MPEG-2} Compressed Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "435--448",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003526",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhatnagar:2009:RRW,
  author =       "Gaurav Bhatnagar and Balasubramanian Raman",
  title =        "Robust Reference-Watermarking Scheme Using Wavelet
                 Packet Transform and Bidiagonal-Singular Value
                 Decomposition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "449--477",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003538",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Huang:2009:FPH,
  author =       "Bo Huang and Naimin Li",
  title =        "Fungiform Papillae Hyperplasia ({FPH}) Identification
                 by Tongue Texture Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "3",
  pages =        "479--494",
  month =        jul,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003496",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shi:2009:SBI,
  author =       "Weiren Shi and Zuojin Li and Xin Shi and Zhi Zhong",
  title =        "A Survey of Biologically Inspired Image Processing for
                 Objects Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "495--510",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946780900354X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wong:2009:PPP,
  author =       "Alexander Wong",
  title =        "{PECSI}: a Practical Perceptually-Enhanced Compression
                 Framework for Still Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "511--529",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003551",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zheng:2009:MTM,
  author =       "Liying Zheng and Kuifeng Liu and Lei Yu",
  title =        "Multilevel Thresholding Method Based on Normalized
                 Cut",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "531--540",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003563",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dinesh:2009:NPA,
  author =       "R. Dinesh and D. S. Guru",
  title =        "Non-Parametric Adaptive Approach for the Detection of
                 Dominant Points on Boundary Curves Based on
                 Non-Symmetric Region of Support",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "541--557",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003575",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Vyas:2009:GTI,
  author =       "Vibha S. Vyas and Priti P. Rege",
  title =        "Geometric Transform Invariant Texture Analysis with
                 Modified {Chebyshev} Moments Based Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "559--574",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003587",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2009:IIC,
  author =       "Satish Kumar Singh and Shishir Kumar",
  title =        "Improved Image Compression Based on Feed-Forward
                 Adaptive Downsampling Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "575--589",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003605",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chang:2009:CSD,
  author =       "Jian Chang and Xiaosong Yang and Jian J. Zhang",
  title =        "Continuous Skeleton-Driven Skinning --- a General
                 Approach For Modeling Skin Deformation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "591--608",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003599",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2009:MCM,
  author =       "J. Wang and N. V. Patel and W. I. Grosky and F.
                 Fotouhi",
  title =        "Moving Camera Moving Object Segmentation in Compressed
                 Video Sequences",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "609--627",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003617",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2009:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 9)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "9",
  number =       "4",
  pages =        "629--631",
  month =        oct,
  year =         "2009",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467809003617",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hossain:2010:VIC,
  author =       "Md. Shafaeat Hossain and Khandaker Abir Rahman and Md.
                 Hasanuzzaman and M. A. Bhuyian and H. Ueno",
  title =        "Video Image Clustering Based on Human Face and Shirt
                 Color",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "1--19",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003639",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Heidary:2010:SSD,
  author =       "Kaveh Heidary and H. John Caulfield",
  title =        "Spectral Sensitivity Design for Optical Sensors",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "21--39",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003640",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2010:CSF,
  author =       "Xiaoping Wang and Shenglan Liu and Liyan Zhang",
  title =        "Constructing Surface Features Through Deformation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "41--56",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003652",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jlassi:2010:DBV,
  author =       "Hejer Jlassi and Kamel Hamrouni",
  title =        "Detection of Blood Vessels in Retinal Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "57--72",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003664",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2010:EPP,
  author =       "Yong-Jin Liu",
  title =        "On the Evaluation of Progressive Point-Sampled
                 Geometry",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "73--91",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003676",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nigam:2010:FPB,
  author =       "Chhabi Nigam and R. Venkatesh Babu and S. Kumar Raja
                 and K. R. Ramakrishnan",
  title =        "Fragmented Particles-Based Robust Object Tracking with
                 Feature Fusion",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "93--112",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003688",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{El-Sattar:2010:NPC,
  author =       "Hussein Karam Hussein Abd El-Sattar",
  title =        "A New Plot\slash Character-Based Interactive System
                 for Story-Based Virtual Reality Applications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "113--133",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781000369X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bourouis:2010:SMB,
  author =       "Sami Bourouis and Kamel Hamrouni",
  title =        "{$3$D} Segmentation of {MRI} Brain Using Level Set and
                 Unsupervised Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "1",
  pages =        "135--154",
  month =        jan,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003706",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ali:2010:VFS,
  author =       "Wajid Ali and Tangui Morvan and Petter Risholm and Ole
                 Jakob Elle and Eigil Samset",
  title =        "A Visualization and Fusion System for Image Guided
                 {RFA} Procedures",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "155--174",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003718",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nesme:2010:AIA,
  author =       "Matthieu Nesme and Fran{\c{c}}ois Faure and Yohan
                 Payan",
  title =        "Accurate Interactive Animation of Deformable Models At
                 Arbitrary Resolution",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "175--202",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781000372X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{DeVisser:2010:DNG,
  author =       "Hans {De Visser} and Josh Passenger and David Conlan
                 and Christoph Russ and David Hellier and Mario Cheng
                 and Oscar Acosta and S{\'e}bastien Ourselin and Olivier
                 Salvado",
  title =        "Developing a Next Generation Colonoscopy Simulator",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "203--217",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003731",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sherstyuk:2010:SAS,
  author =       "Andrei Sherstyuk and Anton Treskunov and Benjamin
                 Berg",
  title =        "Semi-Automatic Surface Scanner for Medical Tangible
                 User Interfaces",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "219--233",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003743",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nguyen:2010:TBR,
  author =       "Van-Hanh Nguyen and Frederic Merienne and Jean-Luc
                 Martinez",
  title =        "Training Based on Real-Time Motion Evaluation for
                 Functional Rehabilitation in Virtual Environment",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "235--250",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003755",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kaur:2010:HED,
  author =       "Amandeep Kaur and Chandan Singh",
  title =        "A Hybrid Edge Detector Using Fuzzy Logic and
                 Mathematical Morphology",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "251--272",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003767",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2010:DTC,
  author =       "Zhaohui Yang and Naimin Li",
  title =        "Detection of Tongue Crack Based on Distant Gradient
                 and Prior Knowledge",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "273--288",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003779",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wu:2010:SBM,
  author =       "Jie Wu and Jiabi Chen and Xuelong Zhang and Jinghai
                 Chen",
  title =        "The Segmentation of Brain {MR} Images Using
                 Reformative Expectation--Maximization Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "2",
  pages =        "289--297",
  month =        apr,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003780",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Aouat:2010:MDN,
  author =       "Saliha Aouat and Slimane Larabi",
  title =        "Matching Descriptors of Noisy Outline Shapes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "299--325",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003792",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Karthigaikumar:2010:PPV,
  author =       "P. Karthigaikumar and K. Baskaran",
  title =        "Partially Pipelined {VLSI} Implementation of
                 {Blowfish} Encryption\slash Decryption Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "327--341",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003809",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Soderstrom:2010:RBH,
  author =       "Ulrik S{\"o}derstr{\"o}m and Haibo Li",
  title =        "Representation Bound for Human Facial Mimic with the
                 Aid of Principal Component Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "343--363",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003810",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gowda:2010:ANI,
  author =       "Rahul Gowda and Shalin M. Mehta and Yue Yang and
                 Baoxin Li",
  title =        "Adaptive Nonlinear Image Enhancement of {Gaussian}
                 Degraded Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "365--393",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003822",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Matungka:2010:EII,
  author =       "Rittavee Matungka and Yuan F. Zheng and Robert L.
                 Ewing",
  title =        "Efficient Invariant Image Registration Utilizing
                 Pre-Shifted Logarithmic Spiral",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "395--421",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003834",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dinesh:2010:CTS,
  author =       "R. Dinesh and D. S. Guru",
  title =        "Concept of Triangular Spatial Relationship and
                 {B}-Tree for Partially Occluded Object Recognition: an
                 Efficient and Robust Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "423--448",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003846",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bai:2010:HPT,
  author =       "Xiaoliang Bai and Shusheng Zhang",
  title =        "Hierarchical Parameterization of Triangular Mesh with
                 a Boundary Polygon Triangulation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "449--466",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003858",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Miyazaki:2010:CDM,
  author =       "Ryuji Miyazaki and Koichi Harada",
  title =        "Creating the Displacement Mapped Low-Level Mesh and
                 Its Application for {CG} Software",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "3",
  pages =        "467--480",
  month =        jul,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781000386X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 31 08:38:02 MDT 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Treuillet:2010:OIV,
  author =       "Sylvie Treuillet and Eric Royer",
  title =        "Outdoor\slash Indoor Vision-Based Localization for
                 Blind Pedestrian Navigation Assistance",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "481--496",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003937",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jovanova:2010:OAS,
  author =       "Blagica Jovanova and Ivica Arsov and Marius Preda and
                 Fran{\c{c}}oise Preteux",
  title =        "Online Animation System for Practicing Cued Speech",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "497--512",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003925",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Durette:2010:SRT,
  author =       "Barth{\'e}l{\'e}my Durette and Jeanny H{\'e}rault and
                 David Alleysson",
  title =        "Simulation of the Retina: a Tool for Visual
                 Prostheses",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "513--529",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003949",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dramas:2010:AVB,
  author =       "Florian Dramas and Simon J. Thorpe and Christophe
                 Jouffrais",
  title =        "Artificial Vision for the Blind: a Bio-Inspired
                 Algorithm for Objects and Obstacles Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "531--544",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003871",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pissaloux:2010:IMO,
  author =       "Edwige Pissaloux and Yong Chen and Ramiro Velazquez",
  title =        "Image Matching Optimization Via Vision and Inertial
                 Data Fusion: Application to Navigation of the Visually
                 Impaired",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "545--558",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003913",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kulkarni:2010:ITU,
  author =       "Shivali D. Kulkarni and Ameya K. Naik and Nitin S.
                 Nagori",
  title =        "{$2$D} Image Transmission Using Bandwidth Efficient
                 Mapping Technique",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "559--573",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003883",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nassar:2010:NFE,
  author =       "Hamed Nassar and Ghada El-Taweel and Eman Mahmoud",
  title =        "A Novel Feature Extraction Scheme for Human Gait
                 Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "575--587",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003895",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dharwadkar:2010:SSG,
  author =       "Nagaraj V. Dharwadkar and B. B. Amberker",
  title =        "Steganographic Scheme for Gray-Level Image Using Pixel
                 Neighborhood and {LSB} Substitution",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "589--607",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003901",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2010:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 10)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "10",
  number =       "4",
  pages =        "609--611",
  month =        oct,
  year =         "2010",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467810003901",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Dec 9 21:06:32 MST 2010",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nguyen:2011:OTV,
  author =       "Thanh Binh Nguyen and Ashish Khare",
  title =        "Object Tracking of Video Sequences in Curvelet
                 Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "1--20",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811003968",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Suraj:2011:RPC,
  author =       "M. G. Suraj and D. S. Guru and S. Manjunath",
  title =        "Recognition of Postal Codes from Fingerspelling Video
                 Sequence",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "21--41",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781100397X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Abbadeni:2011:TPI,
  author =       "Noureddine Abbadeni and Haikel S. Alhichri and Alaa B.
                 Elmasry",
  title =        "Tackling the Problem of Invariant Texture Retrieval
                 Using Multiple Strategies",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "43--64",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811003981",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2011:MHC,
  author =       "Wenjia Yang and Lihua Dou and Juan Zhan",
  title =        "A Multi-Histogram Clustering Approach Toward {Markov}
                 Random Field for Foreground Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "65--81",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811003993",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Periasamy:2011:ATB,
  author =       "P. S. Periasamy and S. Athinarayanan and K.
                 Duraiswamy",
  title =        "An Adaptive Thresholding-Based Color Reduction
                 Algorithm and Its Applications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "83--101",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004007",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kallel:2011:SMM,
  author =       "Mohamed Kallel and Mohamed-Salim Bouhlel and
                 Jean-Christophe Lapayre",
  title =        "Security of the Medical Media Using a Hybrid and
                 Multiple Watermark Technique",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "103--115",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004019",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Boulila:2011:MAS,
  author =       "Wadii Boulila and Imed Riadh Farah",
  title =        "Multi-Approach Satellite Images Fusion Based on Blind
                 Sources Separation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "117--136",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004020",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Su:2011:ISR,
  author =       "Ya Su and Xinbo Gao",
  title =        "Iterative Shape Refinement in {AAM}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "1",
  pages =        "137--151",
  month =        jan,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004032",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Mar 8 10:11:09 MST 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2011:IQF,
  author =       "Haoting Liu and Jie Li and Zheng Wang and Jian Cheng
                 and Hanqing Lu and Yan Zhao",
  title =        "Image Quality Feedback-Based Adaptive Video Definition
                 Improvement for the Space Manipulation Task",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "153--175",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004044",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Florinabel:2011:MBM,
  author =       "D. Jemi Florinabel and S. Ebenezer Juliet and V.
                 Sadasivam",
  title =        "Multiorientation-Based Multistructure Morphological
                 Inpainting",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "177--193",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004056",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2011:QBC,
  author =       "Yuqing Wang and Ming Zhu and Haochen Pang and Yong
                 Wang",
  title =        "Quaternion Based Color Image Quality Assessment
                 Index",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "195--206",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004111",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nasrollahi:2011:SSV,
  author =       "Kamal Nasrollahi and Thomas B. Moeslund and Mohammad
                 Rahmati",
  title =        "Summarization of Surveillance Video Sequences Using
                 Face Quality Assessment",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "207--233",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004068",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2011:STV,
  author =       "Fuzheng Yang and Shuai Wan",
  title =        "Spatial-Temporal Video Quality Assessment Based on
                 Two-Level Temporal Pooling",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "235--249",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781100407X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2011:IMV,
  author =       "J. X. Yang and D. M. Tan and H. R. Wu",
  title =        "An Impairment Metric for Video Temporal Fluctuation
                 Measure",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "251--264",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004081",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Deng:2011:IQM,
  author =       "Cheng Deng and Jie Li and Yifan Zhang and Dongyu Huang
                 and Lingling An",
  title =        "An Image Quality Metric Based on Biologically Inspired
                 Feature Model",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "265--279",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004093",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lu:2011:NCI,
  author =       "Wen Lu and Lihuo He and Wenjian Tang and Fei Gao and
                 Weilong Hou",
  title =        "A Novel Compressed Images Quality Metric",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "2",
  pages =        "281--292",
  month =        apr,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781100410X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jul 8 14:32:32 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Urolagin:2011:KAR,
  author =       "Siddhaling Urolagin and K. V. Prema and N. V. Subba
                 Reddy",
  title =        "{Kannada} Alphabets Recognition with Application to
                 {Braille} Translation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "293--314",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004159",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{He:2011:SBN,
  author =       "Liqiang He and Guangyong Zhang and Yanyan Zhang",
  title =        "Speeding Up Best Neighborhood Matching Algorithm for
                 High-Definition Image on {GPU} {Platform}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "315--337",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004196",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2011:NTL,
  author =       "Xing-Yuan Wang and Zhi-Feng Chen and Jiao-Jiao Yun",
  title =        "A Novel Two-Level Color Image Retrieval Method",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "339--353",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004184",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bonyadi:2011:NHQ,
  author =       "Mohammad Reza Bonyadi and Mohsen Ebrahimi Moghaddam",
  title =        "A Nonuniform High-Quality Image Compression Method to
                 Preserve User-Specified Compression Ratio",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "355--375",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004123",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kamath:2011:RES,
  author =       "Chandrika Kamath and Omar A. Hurricane",
  title =        "Robust Extraction of Statistics from Images of
                 Material Fragmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "377--401",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004172",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Su:2011:BIR,
  author =       "Liyun Su and Ruihua Liu",
  title =        "Blind Image Restoration with Modified {CMA}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "403--413",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004147",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chia:2011:BUI,
  author =       "Wai Chong Chia and Li Wern Chew and Li-Minn Ang and
                 Kah Phooi Seng",
  title =        "Binary-Uncoded Image and Video Compression Using
                 {SPIHT--ZTR} Coding",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "415--437",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004135",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Alvarez:2011:VLD,
  author =       "Miguel Alvarez and Mar{\'\i}a-Elena Algorri",
  title =        "Vectorization and Line Detection for Automatic Image
                 Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "3",
  pages =        "439--470",
  month =        jul,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004160",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 24 06:48:16 MDT 2011",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rawat:2011:CBR,
  author =       "Sanjay Rawat and Balasubramanian Raman",
  title =        "A Chaos-Based Robust Watermarking Algorithm for
                 Rightful Ownership Protection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "471--493",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004263",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lu:2011:NTB,
  author =       "Huchuan Lu and Dong Wang and Yen-Wei Chen and Hao
                 Chen",
  title =        "A Novel Texture-Based Multi-Linear Analysis Algorithm
                 for Face Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "495--508",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781100424X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Massoptier:2011:CGC,
  author =       "Laurent Massoptier and Avishkar Misra and Arcot Sowmya
                 and Sergio Casciaro",
  title =        "Combining Graph-Cut Technique and Anatomical Knowledge
                 for Automatic Segmentation of Lungs Affected by Diffuse
                 Parenchymal Disease in {HRCT} Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "509--529",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004202",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kudelski:2011:FLE,
  author =       "Dimitri Kudelski and Sophie Viseur and Giovanni
                 Scrofani and Jean-Luc Mari",
  title =        "Feature Line Extraction on Meshes Through Vertex
                 Marking and {$2$D} Topological Operators",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "531--548",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004226",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2011:ISC,
  author =       "Wei Wang and Chi-Kit Ronald Chung",
  title =        "Image Segmentation with Complementary Use of Edge and
                 Region Information",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "549--570",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004275",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Schwartz:2011:IFI,
  author =       "William Robson Schwartz and Helio Pedrini",
  title =        "Improved Fractal Image Compression Based on Robust
                 Feature Descriptors",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "571--587",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004251",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Drew:2011:ICR,
  author =       "Mark S. Drew and Graham D. Finlayson",
  title =        "Improvement of Colorization Realism Via the Structure
                 Tensor",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "589--609",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004214",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Balster:2011:PCR,
  author =       "Eric J. Balster and Benjamin T. Fortener and William
                 F. Turri",
  title =        "Post-Compression Rate-Distortion Development for
                 Embedded Block Coding with Optimal Truncation in
                 {JPEG2000} Imagery",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "611--627",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004238",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2011:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 11)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "11",
  number =       "4",
  pages =        "629--631",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467811004238",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 8 18:48:57 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Helmy:2012:CMC,
  author =       "Tarek Helmy",
  title =        "A Computational Model for Context-Based Image
                 Categorization and Description",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250001",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500015",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "19",
}

@Article{Fu:2012:DRA,
  author =       "Bin Fu and Wenxin Li and Minghui Wu and Rongfeng Li
                 and Zhuoqun Xu",
  title =        "A Document Rectification Approach Dealing with Both
                 Perspective Distortion and Warping Based on Text Flow
                 Curve Fitting",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250002",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500027",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "25",
}

@Article{Prasath:2012:ADS,
  author =       "V. B. Surya Prasath and Arindama Singh",
  title =        "An Adaptive Diffusion Scheme for Image Restoration and
                 Selective Smoothing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250003",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500039",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "18",
}

@Article{Mukherjee:2012:CIU,
  author =       "Dipti Prasad Mukherjee and Nilanjan Ray",
  title =        "Contour Interpolation Using Level-Set Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250004",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500040",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "16",
}

@Article{Rani:2012:FRU,
  author =       "J. Sheeba Rani",
  title =        "Face Recognition Using Hybrid Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250005",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500052",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "27",
}

@Article{Lai:2012:FMI,
  author =       "Shuhua Lai and Fuhua (Frank) Cheng",
  title =        "Fast Mesh Interpolation and Mesh Decomposition with
                 Applications",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250006",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500064",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "18",
}

@Article{Li:2012:HIR,
  author =       "Ping Li and Hanqiu Sun and Jianbing Shen and Chen
                 Huang",
  title =        "{HDR} Image Rerendering Using {GPU}-Based Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250007",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500076",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "19",
}

@Article{Suresh:2012:STP,
  author =       "R. M. Suresh and N. Jayalakshmi",
  title =        "Segmentation and Tracking of Progenitor Cells in Time
                 Lapse Microscopy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "1",
  pages =        "1250008",
  month =        jan,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500088",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 29 07:59:06 MST 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "15",
}

@Article{Nejla:2012:BIR,
  author =       "Gribaa Nejla and Noblet Vincent and Khlifa Nawres and
                 Faisan Sylvain and Hamrouni Kamel",
  title =        "Binary Image Registration Based on Geometric Moments:
                 Application to the Registration of {$3$D} Segmented
                 {CT} Head Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250009",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781250009X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "16",
}

@Article{Kodavalla:2012:DVC,
  author =       "Vijay Kumar Kodavalla and P. G. Krishna Mohan",
  title =        "Distributed Video Coding: Feedback-Free Architecture
                 and Implementation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250010",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500106",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "16",
}

@Article{Xu:2012:FRB,
  author =       "Gang Xu and Huchuan Lu and Zunyi Wang",
  title =        "Face Recognition Based on {GPPBTF} and {LBP} with
                 Classifier Fusion",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250011",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500118",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "16",
}

@Article{Fan:2012:GIC,
  author =       "N. Fan and Cheng Jin",
  title =        "Geometric Invariants Construction for Semantic Scene
                 Understanding from Multiple Views Inspired by the Human
                 Visual System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250012",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781250012X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "14",
}

@Article{Yang:2012:IRB,
  author =       "Shiueng-Bien Yang and Ting-Wen Liang",
  title =        "Image Restoration Based on Smooth Gray-Level Detection
                 and Line Prediction Method for Large Missing Regions",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250013",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500131",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "20",
}

@Article{Feddaoui:2012:IRM,
  author =       "Nadia Feddaoui and Hela Mahersia and Kamel Hamrouni",
  title =        "Iris Recognition Method Based on {Gabor} Filters and
                 Uniform Local Binary Patterns",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250014",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500143",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "20",
}

@Article{Wang:2012:MIS,
  author =       "Haijun Wang and Ming Liu",
  title =        "Medical Images Segmentation Using Active Contours
                 Driven by Global and Local Image Fitting Energy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250015",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500155",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "15",
}

@Article{Santosh:2012:RPS,
  author =       "K. C. Santosh and Cholwich Nattee and Bart Lamiroy",
  title =        "Relative Positioning of Stroke-Based Clustering: a New
                 Approach to Online Handwritten {Devanagari} Character
                 Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "2",
  pages =        "1250016",
  month =        apr,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500167",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 3 08:15:54 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  pagecount =    "25",
}

@Article{Zhi:2012:IIA,
  author =       "Zhanjiang Zhi and Yi Sun",
  title =        "An Image Inpainting Algorithm Based on Energy
                 Minimization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "3",
  pages =        "1250017",
  month =        jul,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500179",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Nov 3 13:35:52 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2012:HBS,
  author =       "Shifeng Li and Meng Yao and Huchuan Lu",
  title =        "Human Body Segmentation in a Static Image with On-Line
                 {AdaBoost} at Multiscale Superpixels",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "3",
  pages =        "1250018",
  month =        jul,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500180",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Nov 3 13:35:52 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2012:ICB,
  author =       "Lihe Zhang and Zhenzhen Liu",
  title =        "Image Cosegmentation Based on Local and Global Level
                 Set Methods",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "3",
  pages =        "1250019",
  month =        jul,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500192",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Nov 3 13:35:52 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jiang:2012:RTO,
  author =       "Ming-Xin Jiang and Zhi-Jing Shao and Hong-Yu Wang",
  title =        "Real-Time Object Tracking Algorithm with Cameras
                 Mounted on Moving Platforms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "3",
  pages =        "1250020",
  month =        jul,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500209",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Nov 3 13:35:52 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2012:TTC,
  author =       "Dong Wang and Gang Yang and Huchuan Lu",
  title =        "Tri-Tracking: Combining Three Independent Views for
                 Robust Visual Tracking",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "3",
  pages =        "1250021",
  month =        jul,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500210",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Nov 3 13:35:52 MDT 2012",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mahjoub:2012:MOR,
  author =       "Mohamed Ali Mahjoub and Malek Abbassi",
  title =        "{$3$D} Mesh Object Retrieval by Discrete and
                 Continuous Hidden {Markov} Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250022",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500222",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2012:IED,
  author =       "Xingyuan Wang and Zhifeng Chen and Xuemei Bao",
  title =        "An Improved Edge-Directed Image Interpolation
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250023",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500234",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pal:2012:CAS,
  author =       "Shyamosree Pal and Rahul Dutta and Partha Bhowmick",
  title =        "Circular Arc Segmentation by Curvature Estimation and
                 Geometric Validation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250024",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500246",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lee:2012:EGC,
  author =       "Jong Kwan Lee and Timothy S. Newman",
  title =        "Exploring {GPU}- and Cluster-Based Improvements for
                 Over-Sampled Volume Ray Casting Opacity Correction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250025",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500258",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Takimoto:2012:ICP,
  author =       "Hironori Takimoto and Seiki Yoshimori and Yasue
                 Mitsukura",
  title =        "Invisible Calibration Pattern for Print-And-Scan Data
                 Hiding Based on Human Visual Perception",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250026",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781250026X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2012:LTI,
  author =       "Shiueng-Bien Yang and Chi-Feng Wu",
  title =        "Locating Text in Images Based on the Smooth Gray-Level
                 Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250027",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500271",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dube:2012:PQT,
  author =       "Mridula Dube and Reenu Sharma",
  title =        "Piecewise Quartic Trigonometric Polynomial {B}-Spline
                 Curves with Two Shape Parameters",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250028",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500283",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Slamu:2012:SFE,
  author =       "Wushour Slamu and Juming Cao and Xinhui Yao",
  title =        "Sharp Features Extraction from Point Clouds",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1250029",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467812500295",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2012:AIV,
  author =       "Anonymous",
  title =        "{Author Index} (Volume 12)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "12",
  number =       "4",
  pages =        "1299001",
  month =        oct,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781299001X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:00 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mohanta:2013:NTS,
  author =       "Partha Pratim Mohanta and Sanjoy Kumar Saha and
                 Bhabatosh Chanda",
  title =        "A Novel Technique for Size Constrained Video
                 Storyboard Generation Using Statistical Run Test and
                 Spanning Tree",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350001:1--1350001:24",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500010",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Melkemi:2013:PAD,
  author =       "Mahmoud Melkemi and Frederic Cordier and Nickolas S.
                 Sapidis",
  title =        "A Provable Algorithm to Detect Weak Symmetry in a
                 Polygon",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350002:1--1350002:28",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500022",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anagnostopoulos:2013:EFS,
  author =       "Vasileios I. Anagnostopoulos and Emmanuel S. Sardis
                 and Theodora A. Varvarigou",
  title =        "Estimation of Frame Sequence Noise with Removal of
                 {JPEG} Artifacts",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350003:1--1350003:31",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500034",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jha:2013:LEU,
  author =       "Rajib Kumar Jha and Prabir Kumar Biswas and B. N.
                 Chatterji",
  title =        "Logo Extraction Using Combined Discrete Wavelet
                 Transform and Dynamic Stochastic Resonance",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350004:1--1350004:21",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500046",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Senapati:2013:LBR,
  author =       "Ranjan K. Senapati and Umesh C. Pati and Kamala K.
                 Mahapatra",
  title =        "Low Bit Rate Image Compression Using Hierarchical
                 Listless Block-Tree {DTT} Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350005:1--1350005:23",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500058",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gupta:2013:QMC,
  author =       "Rajani Gupta and Prashant Bansod and R. S. Gamad",
  title =        "Quality Measure of the Compressed Echo, {X}-Ray and
                 {CT} Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350006:1--1350006:29",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781350006X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mohideen:2013:RBC,
  author =       "Abubacker Kaja Mohideen and Kuttiannan Thangavel",
  title =        "Region-Based Contrast Enhancement of Digital
                 Mammograms Using an Improved Watershed Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "1",
  pages =        "1350007:1--1350007:25",
  month =        jan,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500071",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 6 16:27:18 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Agrawal:2013:P,
  author =       "Anupam Agrawal and R. C. Tripathi and Ellen Yi-Luen Do
                 and M. D. Tiwar",
  title =        "Preface",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813020014",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rautaray:2013:HGR,
  author =       "Siddharth Swarup Rautaray and Anupam Agrawal",
  title =        "Hand Gesture Recognition Towards Vocabulary and
                 Application Independency",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400019",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2013:QBF,
  author =       "Durgesh Singh and Shivendra Shivani and Suneeta
                 Agarwal",
  title =        "Quantization-Based Fragile Watermarking Using
                 Block-Wise Authentication and Pixel-Wise Recovery
                 Scheme for Tampered Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400020",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2013:GAI,
  author =       "Piyush Kumar and Anupam Agrawal",
  title =        "{GPU}-Accelerated Interactive Visualization of {$ 3 D
                 $} Volumetric Data Using {CUDA}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400032",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhale:2013:ESF,
  author =       "Aparna Narendra Bhale and Manish Ratnakar Joshi",
  title =        "Enhancement of Screen Film Mammogram Up to a Level of
                 Digital Mammogram: Experimental Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400044",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{De:2013:SDO,
  author =       "Kanjar De and V. Masilamani",
  title =        "A Spatial Domain Object Separability Based
                 No-Reference Image Quality Measure Using Mean and
                 Variance",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400056",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Verma:2013:ISP,
  author =       "Nishchal K. Verma and Shikha Singh",
  title =        "Image Sequence Prediction Using {ANN} and {RBFNN}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400068",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  keywords =     "artificial neural network (ANN); Canny edge
                 detection-based image comparison metric (CIM); mean
                 structure similarity index measure (MSSIM)",
}

@Article{Singh:2013:HAI,
  author =       "Pankaj Pratap Singh and R. D. Garg",
  title =        "A Hybrid Approach for Information Extraction from High
                 Resolution Satellite Imagery",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781340007X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nigam:2013:FCI,
  author =       "Akriti Nigam and Ajay Indoria and R. C. Tripathi",
  title =        "Fuzzy Clustering of Image Trademark Database and
                 Preprocessing Using Adaptive Filter and
                 {Karhunen--Lo{\`e}ve} Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "2",
  pages =        "??--??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813400081",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 6 10:37:51 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kuijper:2013:CBM,
  author =       "Arjan Kuijper and Ilkka Havukkala",
  title =        "Comparing Bitmapped {MicroRNA} Structure Images Using
                 Mutual Symmetry",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500083",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Carvalho:2013:IVF,
  author =       "Paulo Roberto {De Carvalho, Jr.} and Maikon Cismoski
                 {Dos Santos} and William Robson Schwartz and Helio
                 Pedrini",
  title =        "An Improved View Frustum Culling Method Using Octrees
                 for {$3$D} Real-Time Rendering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500095",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jiji:2013:FMP,
  author =       "C. V. Jiji and Ravi Krishnan Unni",
  title =        "Fusion of Multispectral and Panchromatic Images Based
                 on the Nonsubsampled Contourlet Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500101",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Huang:2013:ADC,
  author =       "Wei Huang and Hongtao Lu",
  title =        "Automatic Defect Classification of {TFT-LCD} Panels
                 with Shape, Histogram and Color Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500113",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{He:2013:RAM,
  author =       "Liwen He and Yong Xu and Yan Chen and Jiajun Wen",
  title =        "Recent Advance on Mean Shift Tracking: a Survey",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500125",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Maitra:2013:MDE,
  author =       "Indra Kanta Maitra and Sanjay Nag and Samir K.
                 Bandyopadhyay",
  title =        "Mammographic Density Estimation and Classification
                 Using Segmentation and Progressive Elimination Method",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500137",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wei:2013:SMO,
  author =       "Jie Wei",
  title =        "Small Moving Object Detection from Infra-Red
                 Sequences",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500149",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dorini:2013:SDF,
  author =       "Leyza Baldo Dorini and Neucimar Jer{\^o}nimo Leite",
  title =        "A Self-Dual Filtering Toggle Operator for Speckle
                 Noise Filtering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500150",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Aug 28 14:00:38 MDT 2013",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mollah:2013:HDB,
  author =       "Ayatullah Faruk Mollah and Subhadip Basu and Mita
                 Nasipuri",
  title =        "Handheld Device-Based Character Recognition System for
                 Camera Captured Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350016",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500162",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ray:2013:PAD,
  author =       "Kumar S. Ray and Bimal Kumar Ray",
  title =        "Polygonal Approximation of Digital Curve Based on
                 Reverse Engineering Concept",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350017",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500174",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2013:ROC,
  author =       "Hao Liu and Guanhua Zhu and Jianning Zhao and Hongbo
                 Qian and Ning Dai",
  title =        "Recognition of Occlusions in {CT} Images Using a
                 Curve-Based Parameterization Method",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350018",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500186",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2013:VBA,
  author =       "Shuenn-Jyi Wang and Chung-Kai Hsieh and Tsorng-Lin
                 Chia",
  title =        "Video-Based Approach for Detecting Prohibited
                 Activities on Sporting Courts",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350019",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500198",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Haddad:2013:SAC,
  author =       "Bashar Haddad and Amin Jarrah",
  title =        "Semi-Automatic Cracks Correction Based on Seam
                 Processing, Stochastic Analysis and Learning Process",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350020",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500204",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2013:SHB,
  author =       "Jia Yang and Chee Kooi Chan and Ameersing Luximon",
  title =        "A Survey on {$3$D} Human Body Modeling for Interactive
                 Fashion Design",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350021",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500216",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dorini:2013:SST,
  author =       "Leyza Baldo Dorini and Neucimar Jer{\^o}nimo Leite",
  title =        "A Scale-Space Toggle Operator for Image
                 Transformations",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1350022",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813500228",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2013:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 13)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "13",
  number =       "4",
  pages =        "1399001",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467813990015",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:32 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yuan:2014:MGD,
  author =       "Yongfeng Yuan and Kuanquan Wang",
  title =        "A Mixed {Gauss} and Directional Distance Filter for
                 Fiber Direction Tracking",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450001",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500016",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prakash:2014:MID,
  author =       "Om Prakash and Ashish Khare",
  title =        "Medical Image Denoising Based on Soft Thresholding
                 Using Biorthogonal Multiscale Wavelet Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450002",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500028",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Angadi:2014:RST,
  author =       "S. A. Angadi and M. M. Kodabagi",
  title =        "A Robust Segmentation Technique for Line, Word and
                 Character Extraction from {Kannada} Text in Low
                 Resolution Display Board Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450003",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781450003X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Batagelo:2014:GBS,
  author =       "Harlen Costa Batagelo and Jo{\~a}o Paulo Gois",
  title =        "{GPU}-Based Sphere Tracing for Radial Basis Function
                 Implicits",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450004",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500041",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Carvalho:2014:HCS,
  author =       "L. E. Carvalho and S. L. Mantelli Neto and A. von
                 Wangenheim and A. C. Sobieranski and L. Coser and E.
                 Comunello",
  title =        "Hybrid Color Segmentation Method Using a Customized
                 Nonlinear Similarity Function",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450005",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500053",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jasim:2014:RTC,
  author =       "Mahmood Jasim and Tao Zhang and Md. Hasanuzzaman",
  title =        "A Real-Time Computer Vision-Based Static and Dynamic
                 Hand Gesture Recognition System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450006",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500065",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Takimoto:2014:FOE,
  author =       "Hironori Takimoto and Hitoshi Yamauchi and Mitsuyoshi
                 Kishihara and Kensuke Okubo",
  title =        "Foreground Object Extraction Based on Interactive
                 Color Saliency Map",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "1--2",
  pages =        "1450007",
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500077",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 6 06:13:40 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tan:2014:SVI,
  author =       "Cheen-Hau Tan and Lap-Pui Chau",
  title =        "Single Viewpoint Image-Driven Simplification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450008",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500089?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ray:2014:PRB,
  author =       "Kumar S. Ray",
  title =        "Pattern Recognition Based on Fuzzy Set and Genetic
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450009",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500090?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kulkarni:2014:GBM,
  author =       "S. B. Kulkarni and Raghavendrarao B. Kulkarni and U.
                 P. Kulkarni and Ravindra S. Hegadi",
  title =        "{GLCM}-Based Multiclass Iris Recognition Using {FKNN}
                 and {KNN}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450010",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500107?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ali:2014:LDF,
  author =       "Haider Ali and Umair Ullah Tariq and Muhammad Abid",
  title =        "Learning Discriminating Features for Gender
                 Recognition of Real World Faces",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450011",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500119?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2014:AIA,
  author =       "Yongmei Liu and Tanakrit Wongwitit and Linsen Yu",
  title =        "Automatic Image Annotation Based on Scene Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450012",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500120?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Roy:2014:MSU,
  author =       "Kaushik Roy and Brian O'Connor and Foysal Ahmad and
                 Mohamed S. Kamel",
  title =        "Multibiometric System Using Level Set, Modified {LBP}
                 and Random Forest",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450013",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500132?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lin:2014:LBF,
  author =       "Jian Lin and Bo Peng and Tianrui Li",
  title =        "A Learning-Based Framework for Supervised and
                 Unsupervised Image Segmentation Evaluation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "3",
  pages =        "1450014",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500144?",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Aug 26 06:23:26 MDT 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bareja:2014:IIB,
  author =       "Milan N. Bareja and Chintan K. Modi",
  title =        "An Improved Iterative Back Projection Based Single
                 Image Super Resolution Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450015",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500156",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wei:2014:MEM,
  author =       "Jie Wei",
  title =        "On {Markov Earth Mover}'s Distance",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450016",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500168",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ma:2014:EDV,
  author =       "Ji Ma and David Murphy and Gregory Provan and Cian
                 O'Mathuna and Michael Hayes",
  title =        "The Evaluation of Direct Volume Rendering-Based
                 Uncertainty Visualization Techniques for {$3$D} Scalar
                 Data",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450017",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781450017X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{AlNachar:2014:REB,
  author =       "Rabih {Al Nachar} and Elie Inaty and Patrick J. Bonnin
                 and Yasser Alayli",
  title =        "A Robust Edge-Based Corner Detector {(EBCD)}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450018",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500181",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jacobsen:2014:IED,
  author =       "C. Robert Jacobsen and Morten Nielsen",
  title =        "Investigation of the Effects of Data Collection on
                 Visual Stylometry",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450019",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500193",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Senapati:2014:ILE,
  author =       "Ranjan Kumar Senapati and Prasanth Mankar",
  title =        "Improved Listless Embedded Block Partitioning
                 Algorithms for Image Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450020",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781450020X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Guo:2014:FLD,
  author =       "Yanyan Guo and Xiangdong Fei and Qijun Zhao",
  title =        "Fingerprint Liveness Detection Using Multiple Static
                 Features and Random Forests",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1450021",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814500211",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2014:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 14)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "14",
  number =       "4",
  pages =        "1499001",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467814990010",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Dec 3 09:27:35 MST 2014",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Suruliandi:2015:EEG,
  author =       "A. Suruliandi and G. Murugeswari and P. Arockia Jansi
                 Rani",
  title =        "Empirical Evaluation of Generic Weighted Cubicle
                 Pattern and {LBP} Derivatives for Abnormality Detection
                 in Mammogram Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550001",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500011",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2015:MOT,
  author =       "Brij Mohan Singh and Rahul Sharma and Debashis Ghosh
                 and Ankush Mittal",
  title =        "Multi-Oriented Text Extraction in Stylistic
                 Documents",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550002",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500023",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{daSilva:2015:EDB,
  author =       "Ricardo Dutra da Silva and Rosane Minghim and Helio
                 Pedrini",
  title =        "{$3$D} Edge Detection Based on {Boolean} Functions and
                 Local Operators",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550003",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500035",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Heickal:2015:CVB,
  author =       "Hasnain Heickal and Tao Zhang and Md. Hasanuzzaman",
  title =        "Computer Vision-Based Real-Time {$3$D} Gesture
                 Recognition Using Depth Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550004",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500047",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Keefer:2015:SDI,
  author =       "Robert Keefer and Nikolaos Bourbakis",
  title =        "A Survey on Document Image Processing Methods Useful
                 for Assistive Technology for the Blind",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550005",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500059",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Filisbino:2015:CRM,
  author =       "Tiene A. Filisbino and Gilson A. Giraldi and Carlos E.
                 Thomaz",
  title =        "Comparing Ranking Methods for Tensor Components in
                 Multilinear and Concurrent Subspace Analysis with
                 Applications in Face Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550006",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500060",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2015:IFB,
  author =       "Shuiwang Li and Qijun Zhao and Xiangdong Fei",
  title =        "An Improved {AM--FM}-Based Approach for Reconstructing
                 Fingerprints from Minutiae",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "1",
  pages =        "1550007",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500072",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:35 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Stando:2015:P,
  author =       "Jacek Stando and Ali Dehghan Tanha and Waralak V.
                 Siricharoen and Yoshiro Imai",
  title =        "Preface",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1502001",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815020015",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kim:2015:DDA,
  author =       "Yejin Kim and Myunggyu Kim",
  title =        "Data-Driven Approach for Human Locomotion Generation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1540001",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781540001X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Javadi:2015:ARI,
  author =       "Mohammad Saleh Javadi and Zulaikha Kadim and Hon Hock
                 Woon and Khairunnisa Mohamed Johari and Norshuhada
                 Samudin",
  title =        "An Automatic Robust Image Registration Algorithm for
                 Aerial Mapping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1540002",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815400021",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hashimoto:2015:VCS,
  author =       "Hideyuki Hashimoto and Yuki Fujibayashi and Hiroki
                 Imamura",
  title =        "{$3$D} Video Communication System by Using {Kinect}
                 and Head Mounted Displays",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1540003",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815400033",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Iwasaki:2015:RRM,
  author =       "Fumiya Iwasaki and Hiroki Imamura",
  title =        "A Robust Recognition Method for Occlusion of Mini
                 Tomatoes Based on Hue Information and the Curvature",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1540004",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815400045",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Engelbrecht:2015:DVI,
  author =       "Louis Engelbrecht and Adele Botha and Ronell Alberts",
  title =        "Designing the Visualization of Information",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1540005",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815400057",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Refaey:2015:BRL,
  author =       "Mohammed A. A. Refaey",
  title =        "Background Ruled-Lines Detection and Removal in
                 Full-Colored Handwritten Image Documents",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "2",
  pages =        "1540006",
  month =        apr,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815400069",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Apr 15 14:00:43 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Latha:2015:LFI,
  author =       "Y. L. Malathi Latha and Munaga V. N. K. Prasad and
                 Banoth Sammulal",
  title =        "Local Feature Integration Method Using Phase
                 Congruency for Palm Print Authentication",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "3",
  pages =        "1550008",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500084",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 12 10:01:17 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Syamsuddin:2015:FFB,
  author =       "Muhammad Rusdi Syamsuddin and Jimwook Kim and Sung-Hee
                 Lee",
  title =        "Force Field-Based Control of Dynamic Particles with
                 User-Specified Paths",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "3",
  pages =        "1550009",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500096",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 12 10:01:17 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2015:HIA,
  author =       "Hao Liu and Hongbo Qian and Ning Dai and Jianning
                 Zhao",
  title =        "Heuristic Initialization for Active Contour Models in
                 {CT\slash MRI} Image Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "3",
  pages =        "1550010",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500102",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 12 10:01:17 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dehshibi:2015:GPW,
  author =       "Mohammad Mahdi Dehshibi and Ali Shirmohammadi and
                 Andrew Adamatzky",
  title =        "On Growing {Persian} Words with {$L$}-Systems: Visual
                 Modeling of {Neyname}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "3",
  pages =        "1550011",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500114",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 12 10:01:17 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shreekanth:2015:HBT,
  author =       "T. Shreekanth and V. Udayashankara",
  title =        "A Histogram-Based Two-Stage Adaptive Character
                 Segmentation for Transcription of Inter-Point {Hindi
                 Braille} to Text",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "3",
  pages =        "1550012",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500126",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 12 10:01:17 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2015:CMS,
  author =       "Shiguang Liu and Dongfang Fan",
  title =        "Computer Modeling and Simulation of Fruit Sunscald",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "3",
  pages =        "1550013",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500138",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 12 10:01:17 MDT 2015",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sidram:2015:ENS,
  author =       "M. H. Sidram and Nagappa U. Bhajantri",
  title =        "An Exploration with Novel Shape Signature of {GMSC}
                 Distance Function to Track the Object",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1550014",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781550014X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhattacharjee:2015:CBH,
  author =       "Debjyoti Bhattacharjee and Ashish Bakshi and Kuntal
                 Ghosh",
  title =        "Comparison Between an {HVS} Inspired Linear Filter and
                 the Bilateral Filter in Performing ``Vision at a
                 Glance'' through Smoothing with Edge Preservation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1550015",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500151",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Peng:2015:UBT,
  author =       "Chao Peng and Bing Fang and Francis Quek and Yong Cao
                 and Seung In Park and Liguang Xie",
  title =        "Upper Body Tracking and {$3$D} Gesture Reconstruction
                 Using Agent-Based Architecture",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1550016",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500163",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Angadi:2015:LWT,
  author =       "S. A. Angadi and M. M. Kodabagi",
  title =        "A Light Weight Text Extraction Technique for Hand-Held
                 Device",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1550017",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500175",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Carvalho:2015:IGB,
  author =       "L. E. Carvalho and S. L. Mantelli Neto and A. C.
                 Sobieranski and E. Comunello and A. von Wangenheim",
  title =        "Improving Graph-Based Image Segmentation Using
                 Nonlinear Color Similarity Metrics",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1550018",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500187",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Saini:2015:SVB,
  author =       "Deepika Saini and Sanjeev Kumar",
  title =        "Stereo Vision-Based Conic Reconstruction Using a
                 Ray-Quadric Intersection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1550019",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815500199",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2015:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 15)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "15",
  number =       "4",
  pages =        "1599001",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467815990016",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:06 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bandyopadhyay:2016:ASB,
  author =       "Oishila Bandyopadhyay and Bhabatosh Chanda and Bhargab
                 B. Bhattacharya",
  title =        "Automatic Segmentation of Bones in {X}-ray Images
                 Based on Entropy Measure",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "1",
  pages =        "1650001",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500017",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:07 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cai:2016:PRL,
  author =       "Haipeng Cai",
  title =        "Parallel Rendering for Legible Illustrative
                 Visualizations of Dense Geometries on Commodity
                 {CPUs}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "1",
  pages =        "1650002",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500029",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:07 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yuan:2016:ADM,
  author =       "Jianjun Yuan and Lipei Liu",
  title =        "Anisotropic Diffusion Model Based on a New Diffusion
                 Coefficient and Fractional Order Differential for Image
                 Denoising",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "1",
  pages =        "1650003",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500030",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:07 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{McGraw:2016:SNN,
  author =       "Tim McGraw and Jisun Kang and Donald Herring",
  title =        "Sparse Non-Negative Matrix Factorization for Mesh
                 Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "1",
  pages =        "1650004",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500042",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:07 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Aswatha:2016:IRS,
  author =       "Shashaank M. Aswatha and Jayanta Mukherjee and Partha
                 Bhowmick",
  title =        "An Integrated Repainting System for Digital
                 Restoration of {Vijayanagara} Murals",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "1",
  pages =        "1650005",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500054",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Feb 26 05:50:07 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Manimehalai:2016:NRR,
  author =       "P. Manimehalai and P. Arockia Jansi Rani",
  title =        "A New Robust Reversible Blind Watermarking in
                 Wavelet-Domain for Color Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "2",
  pages =        "1650006",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500066",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 5 06:44:22 MDT 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pratihar:2016:FDP,
  author =       "Sanjoy Pratihar and Partha Bhowmick",
  title =        "Fast and Direct Polygonization for Gray-Scale Images
                 Using Digital Straightness and Exponential Averaging",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "2",
  pages =        "1650007",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500078",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 5 06:44:22 MDT 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bini:2016:IRU,
  author =       "A. A. Bini and P. Jidesh",
  title =        "Image Restoration Using Adaptive Region-Wise $p$-Norm
                 Filter with Local Constraints",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "2",
  pages =        "1650008",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781650008X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 5 06:44:22 MDT 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Grim:2016:ART,
  author =       "Anna Grim and Timothy O'Connor and Peter J. Olver and
                 Chehrzad Shakiban and Ryan Slechta and Robert
                 Thompson",
  title =        "Automatic Reassembly of Three-Dimensional Jigsaw
                 Puzzles",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "2",
  pages =        "1650009",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500091",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 5 06:44:22 MDT 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fathimal:2016:SSS,
  author =       "P. Mohamed Fathimal and P. Arockia Jansi Rani",
  title =        "{$K$} out of {$N$} Secret Sharing Scheme for Multiple
                 Color Images with Steganography and Authentication",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "2",
  pages =        "1650010",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500108",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 5 06:44:22 MDT 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yuan:2016:PMM,
  author =       "Jianjun Yuan and Jianjun Wang",
  title =        "{Perona--Malik} Model with a New Diffusion Coefficient
                 for Image Denoising",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "2",
  pages =        "1650011",
  month =        apr,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781650011X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 5 06:44:22 MDT 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tomar:2016:LRP,
  author =       "Divya Tomar and Sonali Agarwal",
  title =        "Leaf Recognition for Plant Classification Using Direct
                 Acyclic Graph Based Multi-Class Least Squares Twin
                 Support Vector Machine",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "3",
  pages =        "1650012",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500121",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Nov 16 05:43:38 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sethi:2016:CAD,
  author =       "Gaurav Sethi and B. S. Saini",
  title =        "Computer Aided Diagnosis of Abdomen Diseases Using
                 Curvelet Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "3",
  pages =        "1650013",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500133",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Nov 16 05:43:38 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ladha:2016:RPC,
  author =       "Shamsuddin N. Ladha and Kate Smith-Miles and Sharat
                 Chandran",
  title =        "Realistic Projection on Casual Dual-Planar Surfaces
                 with Global Illumination Compensation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "3",
  pages =        "1650014",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500145",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Nov 16 05:43:38 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sowmyayani:2016:ETR,
  author =       "S. Sowmyayani and P. Arockia Jansi Rani",
  title =        "An Efficient Temporal Redundancy Transformation for
                 Wavelet Based Video Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "3",
  pages =        "1650015",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500157",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Nov 16 05:43:38 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gupta:2016:CAA,
  author =       "Pooja Gupta and Kuldip Pahwa",
  title =        "Clock Algorithm Analysis for Increasing Quality of
                 Digital Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "3",
  pages =        "1650016",
  month =        jul,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500169",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Nov 16 05:43:38 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Caetano:2016:VDU,
  author =       "Felipe Andrade Caetano and Marcelo Bernardes Vieira
                 and Rodrigo Luis de Souza da Silva",
  title =        "A Video Descriptor Using Orientation Tensors and
                 Shape-Based Trajectory Clustering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1650017",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500170",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Raja:2016:EMT,
  author =       "S. P. Raja and A. Suruliandi",
  title =        "Evaluating Multiscale Transform Based Image
                 Compression Using Encoding Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1650018",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500182",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{daSilva:2016:GFA,
  author =       "Fl{\'a}vio Altinier Maximiano da Silva and Helio
                 Pedrini",
  title =        "Geometrical Features and Active Appearance Model
                 Applied to Facial Expression Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1650019",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500194",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Atiampo:2016:UIS,
  author =       "Armand Kodjo Atiampo and Georges Laussane Loum",
  title =        "Unsupervised Image Segmentation with Pairwise {Markov}
                 Chains Based on Nonparametric Estimation of Copula
                 Using Orthogonal Polynomials",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1650020",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500200",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2016:IGZ,
  author =       "Geetika Singh and Indu Chhabra",
  title =        "Integrating Global {Zernike} and Local Discriminative
                 {HOG} Features for Face Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1650021",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500212",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gambhir:2016:NFR,
  author =       "Deepak Gambhir and Meenu Manchanda",
  title =        "A Novel Fusion Rule for Medical Image Fusion in
                 Complex Wavelet Transform Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1650022",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816500224",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2016:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 16)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "16",
  number =       "4",
  pages =        "1699001",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467816990011",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Nov 17 05:56:01 MST 2016",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Al-Naji:2017:CCA,
  author =       "Ali Al-Naji and Javaan Chahl",
  title =        "Contactless Cardiac Activity Detection Based on Head
                 Motion Magnification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "1",
  pages =        "1750001",
  month =        jan,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500012",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 26 07:01:04 MST 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kavitha:2017:WBF,
  author =       "J. Kavitha and P. Arockia Jansi Rani and S.
                 Sowmyayani",
  title =        "Wavelet-Based Feature Vector for Shot Boundary
                 Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "1",
  pages =        "1750002",
  month =        jan,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500024",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 26 07:01:04 MST 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kittisuwan:2017:TID,
  author =       "P. Kittisuwan",
  title =        "Textural Image Denoising Using {Gumbel} Random Vectors
                 in {Gaussian} Noise",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "1",
  pages =        "1750003",
  month =        jan,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500036",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 26 07:01:04 MST 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2017:FBM,
  author =       "Uche A. Nnolim",
  title =        "{FPGA}-Based Multiplier-Less Log-Based Hardware
                 Architectures for Hybrid Color Image Enhancement
                 System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "1",
  pages =        "1750004",
  month =        jan,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500048",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 26 07:01:04 MST 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhat:2017:MIF,
  author =       "Aruna Bhat",
  title =        "Makeup Invariant Face Recognition using Features from
                 Accelerated Segment Test and Eigen Vectors",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "1",
  pages =        "1750005",
  month =        jan,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781750005X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 26 07:01:04 MST 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cejnog:2017:WAR,
  author =       "Luciano W. X. Cejnog and Fernando A. A. Yamada and
                 Marcelo Bernardes Vieira",
  title =        "Wide Angle Rigid Registration Using a Comparative
                 Tensor Shape Factor",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "1",
  pages =        "1750006",
  month =        jan,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500061",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Jan 26 07:01:04 MST 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tian:2017:ISC,
  author =       "Chunwei Tian and Guanglu Sun and Qi Zhang and Weibing
                 Wang and Teng Chen and Yuan Sun",
  title =        "Integrating Sparse and Collaborative Representation
                 Classifications for Image Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "2",
  pages =        "1750007",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:12 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Manchanda:2017:FTB,
  author =       "Meenu Manchanda and Rajiv Sharma",
  title =        "Fuzzy Transform-Based Fusion of Multiple Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "2",
  pages =        "1750008",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:12 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kontakis:2017:SIC,
  author =       "Konstantinos Kontakis and Athanasios G. Malamos and
                 Malvina Steiakaki and Spyros Panagiotakis",
  title =        "Spatial Indexing of Complex Virtual Reality Scenes in
                 the {Web}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "2",
  pages =        "1750009",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:12 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ghislain:2017:ATD,
  author =       "Pandry Koffi Ghislain and Georges Lausanne Loum and
                 Ouattara Nouho",
  title =        "Adaptation of Telegraph Diffusion Equation for Noise
                 Reduction on Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "2",
  pages =        "1750010",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:12 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shah:2017:NMI,
  author =       "Said Khalid Shah",
  title =        "Nonrigid Medical Image Registration Based on Curves",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "2",
  pages =        "1750011",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:12 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Parseh:2017:NCF,
  author =       "Mohammad Javad Parseh and Mojtaba Meftahi",
  title =        "A New Combined Feature Extraction Method for {Persian}
                 Handwritten Digit Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "2",
  pages =        "1750012",
  month =        apr,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:12 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Maity:2017:ODC,
  author =       "Santi P. Maity and Hirak Kumar Maity",
  title =        "Optimality in Distortion Control in Reversible
                 Watermarking Using Genetic Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "3",
  pages =        "1750013",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:13 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Farshi:2017:INR,
  author =       "Taymaz Rahkar Farshi",
  title =        "Image Noise Reduction Method Based on Compatibility
                 with Adjacent Pixels",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "3",
  pages =        "1750014",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:13 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sarfraz:2017:QTN,
  author =       "Muhammad Sarfraz and Shamaila Samreen and Malik Zawwar
                 Hussain",
  title =        "A Quadratic Trigonometric Nu Spline with Shape
                 Control",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "3",
  pages =        "1750015",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:13 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prabhanjan:2017:DLA,
  author =       "S. Prabhanjan and R. Dinesh",
  title =        "Deep Learning Approach for {Devanagari} Script
                 Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "3",
  pages =        "1750016",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:13 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Abdelwahab:2017:IIS,
  author =       "Ahmed A. Abdelwahab",
  title =        "Inter-Image Similarity-Based Fast Adaptive Block Size
                 Vector Quantizer for Image Coding",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "3",
  pages =        "1750017",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:13 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Alcantara:2017:HAC,
  author =       "Marlon F. Alcantara and Helio Pedrini and Yu Cao",
  title =        "Human Action Classification Based on Silhouette
                 Indexed Interest Points for Multiple Domains",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "3",
  pages =        "1750018",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 24 06:24:13 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Maehara:2017:DOR,
  author =       "Seiichi Maehara and Kazuo Ikeshiro and Hiroki
                 Imamura",
  title =        "A $3$-Dimensional Object Recognition Method Using
                 Relationship of Distances and Angles in Feature
                 Points",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1750019",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781750019X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fei:2017:EME,
  author =       "Lunke Fei and Shaohua Teng and Jigang Wu and Imad
                 Rida",
  title =        "Enhanced Minutiae Extraction for High-Resolution
                 Palmprint Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1750020",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500206",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yamada:2017:SBW,
  author =       "F. A. A. Yamada and L. W. X. Cejnog and M. B. Vieira
                 and R. L. S. da Silva",
  title =        "A Shape-Based Weighting Strategy Applied to the
                 Covariance Estimation on {ICP}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1750021",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500218",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2017:FBH,
  author =       "Uche A. Nnolim",
  title =        "{FPGA}-Based Hardware Architecture for Fuzzy
                 Homomorphic Enhancement Based on Partial Differential
                 Equations",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1750022",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781750022X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cheng:2017:HLM,
  author =       "Ruzhong Cheng and Yongjun Zhang and Guoping Wang and
                 Yong Zhao and Rahmatulloev Khusravsho",
  title =        "{Haar}-Like Multi-Granularity Texture Features for
                 Pedestrian Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1750023",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500231",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2017:OAS,
  author =       "Qianwen Li and Zhihua Wei and Cairong Zhao",
  title =        "Optimized Automatic Seeded Region Growing Algorithm
                 with Application to {ROI} Extraction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1750024",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817500243",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2017:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 17)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "17",
  number =       "4",
  pages =        "1799001",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467817990017",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Oct 31 06:37:09 MDT 2017",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wei:2018:AQE,
  author =       "Jie Wei and Lin Zhang and Bingmei M. Fu",
  title =        "Automatic Quantification of Endothelial Nitric Oxide
                 Levels in a Microvessel with and without Tumor Cell
                 Adhesion",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "1",
  pages =        "1850001",
  month =        jan,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500018",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 24 07:10:26 MST 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2018:IST,
  author =       "Huan Wang and Fei Yang and Congcong Zhang and Mingwu
                 Ren",
  title =        "Infrared Small Target Detection Based on Patch Image
                 Model with Local and Global Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "1",
  pages =        "1850002",
  month =        jan,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781850002X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 24 07:10:26 MST 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Verma:2018:IDU,
  author =       "Atul Kumar Verma and Barjinder Singh Saini and
                 Taranjit Kaur",
  title =        "Image Denoising using {Alexander} Fractional Hybrid
                 Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "1",
  pages =        "1850003",
  month =        jan,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500031",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 24 07:10:26 MST 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Divakara:2018:HSA,
  author =       "S. S. Divakara and Sudarshan Patilkulkarni and Cyril
                 Prasanna Raj",
  title =        "High Speed Area Optimized Hybrid {DA} Architecture for
                 {$2$D-DTCWT}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "1",
  pages =        "1850004",
  month =        jan,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500043",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 24 07:10:26 MST 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Domadiya:2018:SFE,
  author =       "Prashant Domadiya and Pratik Shah and Suman K. Mitra",
  title =        "Shadow-Free, Expeditious and Precise, Moving Object
                 Separation from Video",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "1",
  pages =        "1850005",
  month =        jan,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500055",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 24 07:10:26 MST 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Choudhury:2018:SBC,
  author =       "Bismita Choudhury and Patrick Then and Biju Issac and
                 Valliappan Raman and Manas Kumar Haldar",
  title =        "A Survey on Biometrics and Cancelable Biometrics
                 Systems",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "1",
  pages =        "1850006",
  month =        jan,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500067",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jan 24 07:10:26 MST 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Giangreco-Maidana:2018:CPS,
  author =       "Alejandro J. Giangreco-Maidana and Horacio Legal-Ayala
                 and Christian E. Schaerer and Waldemar
                 Villamayor-Venialbo",
  title =        "Contour-Point Signature Shape Descriptor for Point
                 Correspondence",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "2",
  pages =        "1850007",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500079",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Apr 7 18:25:20 MDT 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ghosh:2018:VVE,
  author =       "Swarup Kr Ghosh and Anupam Ghosh and Amlan
                 Chakrabarti",
  title =        "{VEA}: Vessel Extraction Algorithm by Active Contour
                 Model and a Novel Wavelet Analyzer for Diabetic
                 Retinopathy Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "2",
  pages =        "1850008",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500080",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Apr 7 18:25:20 MDT 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chen:2018:VCE,
  author =       "Liang-Hua Chen and Chih-Wen Su",
  title =        "Video Caption Extraction Using Spatio-Temporal
                 Slices",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "2",
  pages =        "1850009",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500092",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Apr 7 18:25:20 MDT 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shreekanth:2018:NDI,
  author =       "T. Shreekanth and M. R. Deeksha and Karthikeya R.
                 Kaushik",
  title =        "A Novel Data Independent Approach for Conversion of
                 Hand Punched {Kannada} {Braille} Script to Text and
                 Speech",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "2",
  pages =        "1850010",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500109",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Apr 7 18:25:20 MDT 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ngom:2018:SDP,
  author =       "Ndeye Fatou Ngom and Cheikh H. T. C. Ndiaye and Oumar
                 Niang and Samba Sidibe",
  title =        "Shape Descriptors for Porous Media Analysis Using
                 Computed Tomography Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "2",
  pages =        "1850011",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500110",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Apr 7 18:25:20 MDT 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wu:2018:IAB,
  author =       "Zhaoqi Wu and Reziwanguli Xiamixiding and Atul
                 Sajjanhar and Juan Chen and Quan Wen",
  title =        "Image Appearance-Based Facial Expression Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "2",
  pages =        "1850012",
  month =        apr,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500122",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Apr 7 18:25:20 MDT 2018",
  bibsource =    "http://ejournals.wspc.com.sg/ijig/ijig.shtml;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Helmy:2018:GFS,
  author =       "Tarek Helmy",
  title =        "A Generic Framework for Semantic Annotation of
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500134",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:48 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500134",
  abstract =     "Advanced digital capturing technologies have led to
                 the explosive growth of images on the Web. To retrieve
                 the desired image from a huge amount of images, textual
                 query is handier to represent the user's interest than
                 providing a visually similar image as a query. Semantic
                 annotation of images' has been identified as an
                 important step towards more efficient manipulation and
                 retrieval of images. The aim of the semantic annotation
                 of images is to annotate the existing images on the Web
                 so that the images are more easily interpreted by
                 searching programs. To annotate the images effectively,
                 extensive image interpretation techniques have been
                 developed to explore the semantic concept of images.
                 But, due to the complexity and variety of backgrounds,
                 effective image annotation is still a very challenging
                 and open problem. Semantic annotation of Web contents
                 manually is not feasible or scalable too, due to the
                 huge amount and rate of emerging Web content. In this
                 paper, we have surveyed the existing image annotation
                 models and developed a hierarchical
                 classification-based image annotation framework for
                 image categorization, description and annotation.
                 Empirical evaluation of the proposed framework with
                 respect to its annotation accuracy shows high precision
                 and recall compared with other annotation models with
                 significant time and cost. An important feature of the
                 proposed framework is that its specific annotation
                 techniques, suitable for a particular image category,
                 can be easily integrated and developed for other image
                 categories.",
  acknowledgement = ack-nhfb,
  articleno =    "1850013",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Benchaou:2018:FSB,
  author =       "Soukaina Benchaou and M'Barek Nasri and Ouafae {El
                 Melhaoui}",
  title =        "Feature Selection Based on Evolution Strategy for
                 Character Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500146",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:48 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500146",
  abstract =     "Handwriting, printed character recognition is an
                 interesting area in image processing and pattern
                 recognition. It consists of a number of phases which
                 are preprocessing, feature extraction and
                 classification. The phase of feature extraction is
                 carried out by different techniques; zoning, profile
                 projection, and ameliored Freeman. The high number of
                 features vector can increase the error rate and the
                 training time. So, to solve this problem, we present in
                 this paper a new method of selecting attributes based
                 on the evolution strategy in order to reduce the
                 feature vector dimension and to improve the recognition
                 rate. The proposed model has been applied to recognize
                 numerals and it obtained a better results and showed
                 more robustness than without the selection system.",
  acknowledgement = ack-nhfb,
  articleno =    "1850014",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Roy:2018:HIR,
  author =       "Aniket Roy and Arpan Kumar Maiti and Kuntal Ghosh",
  title =        "An {HVS} Inspired Robust Non-blind Watermarking Scheme
                 in {YCbCr} Color Space",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500158",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:48 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500158",
  abstract =     "Digital Watermarking is an important tool for
                 copyright protection. A good quality watermarking
                 scheme should provide both perceptual transparency as
                 well as robustness against attacks. Many efficient
                 schemes exist for grayscale image watermarking, but
                 relatively less attention has been paid to watermarking
                 for color images. Moreover, the existing works do not
                 provide adequate justification for the possible choice
                 of color space. In this paper, justification is
                 provided for the choice of YCbCr color space for
                 watermark embedding. A human visual system
                 (HVS)-inspired image-adaptive non-blind watermarking
                 scheme in the YCbCr space has subsequently been
                 proposed. This new algorithm has been referred to as
                 the Additive Embedding Scheme (AES). It comprises of a
                 modified watermarking strength parameter (
                 {\textalpha}mean {\textalpha}mean {\textalpha}mean ),
                 in combination with the discrete wavelet transform and
                 singular value decomposition (DWT-SVD). Experimental
                 results demonstrate that the proposed watermarking
                 scheme in YCbCr color space provides better perceptual
                 quality as well as robustness against attacks as
                 compared to existing schemes. We have further
                 improvised the aforementioned scheme to come up with a
                 Multiplicative Embedding Scheme (MES) for additional
                 robustness against a special type of attack, viz. the
                 Singular Value Exchange Attack.",
  acknowledgement = ack-nhfb,
  articleno =    "1850015",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sacht:2018:RTC,
  author =       "Leonardo Sacht and Diego Nehab and Rodolfo Schulz de
                 Lima",
  title =        "Real-Time Continuous Image Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S021946781850016X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:48 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946781850016X",
  abstract =     "In this work, we propose a framework that performs a
                 number of popular image-processing operations in the
                 continuous domain. This is in contrast to the standard
                 practice of defining them as operations over discrete
                 sequences of sampled values. The guiding principle is
                 that, in order to prevent aliasing, nonlinear
                 image-processing operations should ideally be performed
                 prior to prefiltering and sampling. This is of course
                 impractical, as we may not have access to the
                 continuous input. Even so, we show that it is best to
                 apply image-processing operations over the continuous
                 reconstruction of the input. This transformed
                 continuous representation is then prefiltered and
                 sampled to produce the output. The use of high-quality
                 reconstruction strategies brings this alternative much
                 closer to the ideal than directly operating over
                 discrete values. We illustrate the advantages of our
                 framework with several popular effects. In each case,
                 we demonstrate the quality difference between
                 continuous image-processing, their discrete
                 counterparts and previous anti-aliasing alternatives.
                 Finally, our GPU implementation shows that current
                 graphics hardware has enough computational power to
                 perform continuous image processing in real-time.",
  acknowledgement = ack-nhfb,
  articleno =    "1850016",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2018:ACF,
  author =       "Jinpeng Zhang and Jinming Zhang",
  title =        "An Analysis of {CNN} Feature Extractor Based on {KL}
                 Divergence",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500171",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:48 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500171",
  abstract =     "Convolutional neural networks (CNNs) have brought in
                 exciting progress in many computer vision tasks. But
                 the feature extraction process executed by CNN still
                 keeps a black box to us, and we have not fully
                 understood its working mechanism. In this paper, we
                 propose a method to evaluate CNN features and further
                 to analyze the CNN feature extractor, which is inspired
                 by Bayes Classification Theory and KL divergence (KLD).
                 Experiments have shown that CNN can promote feature
                 discrimativeness by gradually increasing the
                 intra-class KLD, and meanwhile promote feature
                 robustness by gradually decreasing the inner-class KLD
                 during training. Experiments also reveal that, with the
                 deepening of network, CNN can gradually improve
                 separability information density in feature space and
                 encode much more separability information into the
                 final feature vectors.",
  acknowledgement = ack-nhfb,
  articleno =    "1850017",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lu:2018:FVS,
  author =       "Yan Lu and Bin Liu and Weihai Li and Nenghai Yu",
  title =        "Fast Video Stitching for Aerially Captured {HD}
                 Videos",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "3",
  pages =        "??--??",
  month =        jul,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500183",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:48 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500183",
  abstract =     "Videos captured from the air by flying devices like
                 Unmanned Aerial Vehicles (UAVs) have great application
                 prospects in many fields such as journalism, art,
                 military and public security. Due to the difficulties
                 such as vibration, needing for speed and high
                 resolution and so on, it is non-trivial to apply
                 traditional static image stitching algorithms to flying
                 cameras. To this end, we propose a real-time video
                 stitching system which is capable to stitch high
                 definition (HD) videos captured by mobile aerial
                 devices. In our work, we use scale invariant
                 information to speed up the feature point extraction.",
  acknowledgement = ack-nhfb,
  articleno =    "1850018",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Samanta:2018:LTB,
  author =       "Sourav Samanta and Amartya Mukherjee and Amira S.
                 Ashour and Nilanjan Dey and Jo{\~a}o Manuel R. S.
                 Tavares and Wahiba {Ben Abdessalem Kar{\^a}a} and Redha
                 Taiar and Ahmad Taher Azar and Aboul {Ella Hassanien}",
  title =        "Log Transform Based Optimal Image Enhancement Using
                 Firefly Algorithm for Autonomous Mini Unmanned Aerial
                 Vehicle: An Application of Aerial Photography",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467818500195",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500195",
  abstract =     "The Unmanned Aerial Vehicles (UAV) are widely used for
                 capturing images in border area surveillance, disaster
                 intensity monitoring, etc. An aerial photograph offers
                 a permanent recording solution as well. But rapid
                 weather change, low quality image capturing equipments
                 results in low/poor contrast images during image
                 acquisition by Autonomous UAV. In this current study, a
                 well-known meta-heuristic technique, namely, Firefly
                 Algorithm (FA) is reported to enhance aerial images
                 taken by a Mini Unmanned Aerial Vehicle (MUAV) via
                 optimizing the value of certain parameters. These
                 parameters have a wide range as used in the Log
                 Transformation for image enhancement. The entropy and
                 edge information of the images is used as an objective
                 criterion for evaluating the image enhancement of the
                 proposed system. Inconsistent with the objective
                 criterion, the FA is used to optimize the parameters
                 employed in the objective function that accomplishes
                 the superlative enhanced image. A low-light imaging has
                 been performed at evening time to prove the
                 effectiveness of the proposed algorithm. The results
                 illustrate that the proposed method has better
                 convergence and fitness values compared to Particle
                 Swarm Optimization. Therefore, FA is superior to PSO,
                 as it converges after a less number of iterations.",
  acknowledgement = ack-nhfb,
  articleno =    "1850019",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sharma:2018:CSS,
  author =       "Himani Sharma and D. C. Mishra and R. K. Sharma and
                 Naveen Kumar",
  title =        "Crypto-stego System for Securing Text and Image Data",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500201",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500201",
  abstract =     "Conventional techniques for security of data, designed
                 by using only one of the security mechanisms,
                 cryptography or steganography, are suitable for limited
                 applications only. In this paper, we propose a
                 crypto-stego system that would be appropriate for
                 secure transmission of different forms of data. In the
                 proposed crypto-stego system, we present a mechanism to
                 provide secure transmission of data by multiple safety
                 measures, firstly by applying encryption using Affine
                 Transform and Discrete Cosine Transform (DCT) and then
                 merging this encrypted data with an image, randomly
                 chosen from a set of available images, and sending the
                 image so obtained to the receiver at the other end
                 through the network. The data to be sent over a
                 communication channel may be a gray-scale or colored
                 image, or a text document ({.doc}, {.txt}, or {.pdf}
                 file). As it is encrypted and sent hidden in an image,
                 it avoids any attention to itself by the observers in
                 the network. At the receiver's side, reverse
                 transformations are applied to obtain the original
                 information. The experimental results, security
                 analysis and statistical analysis for gray-scale
                 images, RGB images, text documents ({.doc}, {.txt},
                 {.pdf} files), show robustness and appropriateness of
                 the proposed crypto-stego system for secure
                 transmission of the data through unsecured network. The
                 security analysis and key space analysis demonstrate
                 that the proposed technique is immune from
                 cryptanalysis.",
  acknowledgement = ack-nhfb,
  articleno =    "1850020",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Talbi:2018:SIW,
  author =       "Mourad Talbi and Med Salim Bouhlel",
  title =        "Secure Image Watermarking Based on {LWT} and {SVD}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500213",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500213",
  abstract =     "Nowadays, digital watermarking is employed for
                 authentication and copyright protection. In this paper,
                 a secure image watermarking scheme based on lifting
                 wavelet transform (LWT) and singular value
                 decomposition (SVD), is proposed. Both LWT and SVD are
                 used as mathematical tools for embedding watermark in
                 the host image. In this work, the watermark is a speech
                 signal which is segmented into shorted portions having
                 the same length. This length is equal to 256 and these
                 different portions constitute the different columns of
                 a speech image. The latter is then embedded into a
                 grayscale or color image (the host image). This
                 procedure is performed in order to insert into an image
                 a confidential data which is in our case a speech
                 signal. But instead of embedding this speech signal
                 directly into the image, we transform it into a matrix
                 and treated it as an image (``a speech image''). Of
                 course, this speech signal transformation permits us to
                 use LWT-2D and SVD to both the host image and the
                 watermark (``a speech image''). The proposed technique
                 is applied to a number of grayscale and color images.
                 The obtained results from peak signal-to-noise ratio
                 (PSNR) and structural similarity (SSIM) computations
                 show the performance of the proposed technique.
                 Experimental evaluation also shows that the proposed
                 scheme is able to withstand a number of attacks such as
                 JPEG compression, mean and median attacks. In our
                 evaluation of the proposed technique, we used another
                 technique of secure image watermarking based on DWT-2D
                 and SVD.",
  acknowledgement = ack-nhfb,
  articleno =    "1850021",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jangid:2018:HDS,
  author =       "Mahesh Jangid and Sumit Srivastava",
  title =        "Handwritten {Devanagari} Similar Character Recognition
                 by {Fisher} Linear Discriminant and Pairwise
                 Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500225",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500225",
  abstract =     "The research works in Handwritten Devanagari
                 Characters are continually evolving into new
                 challenges, which exposed the new sources of further
                 research work like, character normalization, gray-level
                 normalization, a discrimination of the similar
                 characters and many more. This paper discusses the
                 discrimination of the similar characters, which is one
                 of the major sources of classification error. The
                 similar shape character has a very minute difference,
                 which is called critical region and used to
                 discriminate them by human beings. The primary goal of
                 the current work is to identify the critical region of
                 the similar character and use the same to generate
                 additional features in order to minimize the
                 classification errors in the end results. It is also
                 quite challenging to identify the critical region as
                 the characters are written in different handwriting
                 styles and fonts. The paper suggests the Fisher linear
                 discriminant model to detect the critical region, which
                 is used to extract the additional feature. The
                 experiments work was conducted on the standard
                 database, which has 36 172 handwritten Devanagari
                 characters and significant improvement has been
                 recorded by the aforesaid technique.",
  acknowledgement = ack-nhfb,
  articleno =    "1850022",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Salehi:2018:RHF,
  author =       "Hadi Salehi and Javad Vahidi and Homayun Motameni",
  title =        "A Robust Hybrid Filter Based on Evolutionary
                 Intelligence and Fuzzy Evaluation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500237",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500237",
  abstract =     "In this paper, a novel denoising method based on
                 wavelet, extended adaptive Wiener filter and the
                 bilateral filter is proposed for digital images.
                 Production of mode is accomplished by the genetic
                 algorithm. The proposed extended adaptive Wiener filter
                 has been developed from the adaptive Wiener filter.
                 First, the genetic algorithm suggest some hybrid
                 models. The attributes of images, including peak signal
                 to noise ratio, signal to noise ratio and image quality
                 assessment are studied. Then, in order to evaluate the
                 model, the values of attributes are sent to the Fuzzy
                 deduction system. Simulations and evaluations mentioned
                 in this paper are accomplished on some standard images
                 such as Lena, boy, fruit, mandrill, Barbara, butterfly,
                 and boat. Next, weaker models are omitted by studying
                 of the various models. Establishment of new generations
                 performs in a form that a generation emendation is
                 carried out, and final model has a more optimum quality
                 compared to each two filters in order to obviate the
                 noise. At the end, the results of this system are
                 studied so that a comprehensive model with the best
                 performance is to be found. Experiments show that the
                 proposed method has better performance than wavelet,
                 bilateral, Butterworth, and some other filters.",
  acknowledgement = ack-nhfb,
  articleno =    "1850023",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kittisuwan:2018:TRD,
  author =       "Pichid Kittisuwan",
  title =        "Textural Region Denoising: Application in
                 Agriculture",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818500249",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818500249",
  abstract =     "Geo-science and remote sensing technologies play
                 enormous roles in agriculture nowadays, especially in
                 analysis of data from aerial images such as satellite
                 images and drone images. Most agricultural images
                 contain more textural regions than non-textural
                 regions. Therefore, data management in terms of
                 textural regions is very important. Indeed, additive
                 white Gaussian noise (AWGN) is the fundamental problem
                 in digital image analysis. In wavelet transform,
                 Bayesian estimation is useful in several noise
                 reduction methods. The Bayesian technique requires a
                 prior modeling of noise-free wavelet coefficients. In
                 non-textural regions, the wavelet coefficients might be
                 better modeled by super-Gaussian density such as
                 Laplacian, Pearson type VII, Cauchy, and two-sided
                 gamma distributions. However, the statistical model of
                 textural regions is Gaussian model. Therefore, in
                 agricultural images, we require flexible model between
                 super-Gaussian and Gaussian models. In fact, the
                 generalized Gaussian distribution (GGD) is the suitable
                 model for this problem. Therefore, we present new
                 maximum a posteriori (MAP) estimator for spacial case
                 of GGD in AWGN. Here, we obtained the analytical form
                 solution. Moreover, this research work will also
                 describe limitations of GGD application in Bayesian
                 estimator. The simulation results illustrate that our
                 presented method outperforms the state-of-the-art
                 methods qualitatively and quantitatively.",
  acknowledgement = ack-nhfb,
  articleno =    "1850024",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2018:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 18)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "18",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2018",
  DOI =          "https://doi.org/10.1142/S0219467818990012",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Nov 9 06:55:50 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467818990012",
  acknowledgement = ack-nhfb,
  articleno =    "1899001",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arani:2019:HFW,
  author =       "Seyed Ali Asghar Abbaszadeh Arani and Ehsanollah Kabir
                 and Reza Ebrahimpour",
  title =        "Handwritten {Farsi} Word Recognition Using {NN}-Based
                 Fusion of {HMM} Classifiers with Different Types of
                 Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467819500013",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 14 06:31:30 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500013",
  abstract =     "In this paper, an off-line method, based on hidden
                 Markov model, HMM, is used for holistic recognition of
                 handwritten words of a limited vocabulary. Three
                 feature sets based on image gradient, black--white
                 transition and contour chain code are used. For each
                 feature set an HMM is trained for each word. In the
                 recognition step, the outputs of these classifiers are
                 combined through a multilayer perceptron, MLP. High
                 number of connections in this network causes a
                 computational complexity in the training. To avoid this
                 problem, a new method is proposed. In the experiments
                 on 16000 images of 200 names of Iranian cities, from
                 ``Iranshahr 3'' dataset, the results of the proposed
                 method are presented and compared with some similar
                 methods. An error analysis on these results is also
                 provided.",
  acknowledgement = ack-nhfb,
  articleno =    "1950001",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dutta:2019:ISB,
  author =       "P. K. Dutta",
  title =        "Image Segmentation Based Approach for the Purpose of
                 Developing Satellite Image Spatial Information
                 Extraction for Forestation and River Bed Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500025",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 14 06:31:30 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500025",
  abstract =     "Classification of remote sensing spatial information
                 from multi spectral satellite imagery can be used to
                 obtain multiple representation of the image and capture
                 different structure lineaments. Pixels are grouped
                 using clustering and morphology based segmentation for
                 region based spatial information. This is used to
                 calculate the spatial features of the contiguous
                 regions by classifying the region into the statistics
                 of the pixel properties. In the proposed work, analysis
                 of Google Earth images for identification of
                 morphological patterns of the river flow is done for
                 remote sensing image using graph-cuts. Multi-temporal
                 satellite images acquired from Google Earth to identify
                 the digital elevation is used to formulate the energy
                 function from images to compare the displacement in
                 pixel value using similarity measure. A method is
                 proposed to solve non-rigid image transformation via
                 graph-cuts algorithm by modeling the registration
                 process as a discrete labeling problem. A displacement
                 vector associated to each pixel in the source image
                 indicates the corresponding position in the moving
                 image. The transformation matrix produced from change
                 in the intensity of the pixels for a region is then
                 optimized for energy minimization by using the
                 graph-cuts algorithm and demon registration technique.
                 The proposed study enhances the advantages of regional
                 segmentation in order to know homogeneous areas for
                 optimal image segmentation and digital footprints for
                 change in the river bed patterns by identifying the
                 change in LANDSAT data from temporal satellite images.
                 By applying the proposed multi-level registration
                 method, the number of labels used in each level is
                 greatly reduced due to lower image resolution being
                 used in coarser levels. The results demonstrate that
                 the lineament detection for better accuracy compared to
                 traditional sources of lineament identification
                 methods. It has provided better geotectonic
                 understanding of Cudappah rock in Ahobhilam with
                 Quartzite. The imprints of Eastern Ghat orogeny are
                 seen in upper stream section through a graph cut based
                 segmentation approach.",
  acknowledgement = ack-nhfb,
  articleno =    "1950002",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2019:FFD,
  author =       "Uche A. Nnolim",
  title =        "Formulation of Fractional Derivative-Based De-Hazing
                 Algorithm and Implementation on Mobile-Embedded
                 Devices",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500037",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 14 06:31:30 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500037",
  abstract =     "This paper presents the modification of a previously
                 developed algorithm using fractional order calculus and
                 its implementation on mobile-embedded devices such as
                 smartphones. The system performs enhancement on three
                 categories of images such as those exhibiting uneven
                 illumination, faded features/colors and hazy
                 appearance. The key contributions include the
                 simplified scheme for illumination correction, contrast
                 enhancement and de-hazing using fractional
                 derivative-based spatial filter kernels. These are
                 achieved without resorting to logarithmic image
                 processing, histogram-based statistics and complex
                 de-hazing techniques employed by conventional
                 algorithms. The simplified structure enables ease of
                 implementation of the algorithm on mobile devices as an
                 image processing application. Results indicate that the
                 fractional order version of the algorithm yields good
                 results relative to the integer order version and other
                 algorithms from the literature.",
  acknowledgement = ack-nhfb,
  articleno =    "1950003",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Biswas:2019:CPM,
  author =       "Biswajit Biswas and Biplab Kanti Sen",
  title =        "Color {PET-MRI} Medical Image Fusion Combining
                 Matching Regional Spectrum in Shearlet Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500049",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 14 06:31:30 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500049",
  abstract =     "The color PET-MRI medical image fusion is a growing
                 research area in medical image processing domain. MRI
                 imagery provides the picture of the anatomy of brain
                 tissues without any functional information, while the
                 color PET imagery gives the functional information of
                 brain tissues with low spatial resolution. An ideal
                 fusion model should maintain both the functional and
                 spatial information of the images without any spatial
                 distortion or color deformation. In this work, we
                 present a novel fusion technique for color PET-MRI
                 medical images using Two-Dimensional Discrete Fourier
                 (2DFT)-Karhunen--Loeve transform (KLT) and singular
                 value decomposition (SVD) in shearlet domain. This
                 method decomposes the source images into multi-scaled
                 and multi-directional sub-bands by shearlet transform
                 (ST). Then, SVD is utilized to eliminate superfluous ST
                 coefficients; later, the 2DFT and KLT are utilized to
                 estimate optimal low-pass ST coefficients in each of
                 the decomposed images. Later, we combine the largest
                 low-pass ST coefficients using a novel fusion strategy.
                 The process of decomposing the source image has been
                 discussed in detail. Finally, we use the inverse
                 shearlet transformation (IST) to obtain the fused
                 image. Experimental results establish the excellence of
                 our proposed method in terms of quantitative and
                 qualitative evaluation criteria compared to other
                 state-of-the-art techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "1950004",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Baig:2019:RTF,
  author =       "M. Amir Baig and Athar A. Moinuddin and Ekram Khan",
  title =        "Real-Time Fidelity Measurement of {JPEG2000} Coded
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500050",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 14 06:31:30 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500050",
  abstract =     "The progressive nature of the JPEG2000 coded bitstream
                 allows the reconstruction of images of different
                 qualities from a single coded bitstream. This feature
                 is utilized in this work to estimate the
                 mean-squared-error (MSE) of reconstructed images
                 without requiring the original image. It is based on
                 the fact that if the MSE between the original image and
                 a lower quality image is known, the MSE for higher
                 quality images can be estimated from a quality scalable
                 bitstream. The proposed method is highly accurate and
                 is very simple as no complex statistical modeling is
                 needed. Therefore, it is suitable to measure the
                 fidelity of JPEG2000 decoded images at any desired
                 quality in a real-time scenario.",
  acknowledgement = ack-nhfb,
  articleno =    "1950005",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Halder:2019:SSP,
  author =       "Amiya Halder and Sayan Halder and Samrat Chakraborty
                 and Apurba Sarkar",
  title =        "A Statistical Salt-and-Pepper Noise Removal
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "1",
  pages =        "??--??",
  month =        jan,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500062",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 14 06:31:30 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500062",
  abstract =     "This paper proposes a novel approach to remove
                 salt-and-pepper noise from a given noisy image. The
                 proposed algorithm is based on statistical quantities
                 such as mean and standard deviation. It determines the
                 intensity to be placed on the impulse point by
                 calculating the eligibility of the nearby points in a
                 very simple way. This method works iteratively and
                 removes all the impulse points restoring the edges and
                 minute details. The proposed algorithm is very
                 efficient and gives better results than various
                 existing algorithms. The performance of the proposed
                 method are compared with other existing methods with
                 images of noise density as high as 99\% and is found to
                 perform better.",
  acknowledgement = ack-nhfb,
  articleno =    "1950006",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Oliveira:2019:NTB,
  author =       "Walter Alexandre A. de Oliveira and Denise Guliato and
                 Douglas {Coelho Braga de Oliveira} and Rodrigo Luis de
                 {Souza da Silva} and Gilson Antonio Giraldi",
  title =        "New Technique for Binary Morphological Shape-Based
                 Interpolation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "2",
  pages =        "??--??",
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467819500074",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 10 09:47:18 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500074",
  abstract =     "In this paper we consider shape-based methods to
                 generate additional slices in 3D binary volumes. The
                 focused interpolation approaches, named SIMOL and BORS,
                 are based on morphological and logical operators. Given
                 two adjacent slices S1 and S2 of the binary image set,
                 the methods iteratively generate a sequence of new
                 slices showing a gradual transition between the
                 corresponding shapes. First, we analyze the SIMOL and
                 BORS techniques and highlight their problems. Then we
                 present the main contribution of this paper: a new
                 interpolation scheme, called SIMOL-NEW, that combines
                 the iterative scheme of BORS and an interpolation
                 kernel generated through SIMOL framework. Next, we
                 compare SIMOL-NEW and BORS approaches using theoretical
                 elements and computational experiments. The latter are
                 executed using: (a) benchmark shapes; (b) simple
                 volumes defined by sphere and paraboloid; (c)
                 combination of ellipsoids; (d) a fork-like volume; (e)
                 Cylinder Minus Sphere. The conclusion is that SIMOL-NEW
                 performs closer to BORS for the cases (a) and (c) but
                 it is more accurate than BORS in the tests (b) and (d).
                 Besides, we offer comparisons of state-of-the-art
                 approaches in shape-based interpolation and SIMOL-NEW
                 using ground truth volumes (d) and (e). The
                 computational experiment report that SIMOL-NEW gets
                 outstanding results regarding the ability to recover
                 the target volume.",
  acknowledgement = ack-nhfb,
  articleno =    "1950007",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dixit:2019:FBD,
  author =       "Umesh D. Dixit and M. S. Shirdhonkar",
  title =        "Fingerprint-Based Document Image Retrieval",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "2",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500086",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 10 09:47:18 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500086",
  abstract =     "Most of the documents use fingerprint impression for
                 authentication. Property related documents, bank
                 checks, application forms, etc., are the examples of
                 such documents. Fingerprint-based document image
                 retrieval system aims to provide a solution for
                 searching and browsing of such digitized documents. The
                 major challenges in implementing fingerprint-based
                 document image retrieval are an efficient method for
                 fingerprint detection and an effective feature
                 extraction method. In this work, we propose a method
                 for automatic detection of a fingerprint from given
                 query document image employing Discrete Wavelet
                 Transform (DWT)-based features and SVM classifier. In
                 this paper, we also propose and investigate two feature
                 extraction schemes, DWT and Stationary Wavelet
                 Transform (SWT)-based Local Binary Pattern (LBP)
                 features for fingerprint-based document image
                 retrieval. The standardized Euclidean distance is
                 employed for matching and ranking of the documents.
                 Proposed method is tested on a database of 1200
                 document images and is also compared with current
                 state-of-art. The proposed scheme provided 98.87\% of
                 detection accuracy and 73.08\% of Mean Average Precision
                 (MAP) for document image retrieval.",
  acknowledgement = ack-nhfb,
  articleno =    "1950008",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{BinMortuza:2019:KCB,
  author =       "Fahad {Bin Mortuza}",
  title =        "Kernel-Coefficient-Based Feature Method for Face
                 Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "2",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500098",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 10 09:47:18 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500098",
  abstract =     "A kernel-coefficient-based feature method is proposed
                 to detect faces. The proposed method uses a
                 mathematical expression and 26 different arrangements
                 of kernel-coefficients of a kernel (testing region).
                 The method manipulates the symmetric appearance of a
                 face with respect to a rigid-kernel (fixed region). The
                 expression, which is used to generate feature values,
                 responds to pixels on edges of the image-objects only.
                 For each distinct arrangement of kernel-coefficients, a
                 feature-value is generated. The objective of the
                 proposed kernel-coefficient-based feature method is to
                 reduce the number of feature values required for face
                 detection.",
  acknowledgement = ack-nhfb,
  articleno =    "1950009",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Guillen-Reyes:2019:BLD,
  author =       "Fernando O. Guill{\'e}n-Reyes and Francisco J.
                 Dom{\'\i}nguez-Mota",
  title =        "Boundary Layer Detection Techniques Applied to Edge
                 Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "2",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500104",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 10 09:47:18 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500104",
  abstract =     "In this paper, we describe a novel algorithm for edge
                 detection on a digital image, which is based locally on
                 the directional averaged gradient properties of the
                 intensity function, and produces very satisfactory
                 results in high-resolution digital images in low
                 execution time. Several examples show results which are
                 comparable to those obtained by Canny and Sobel
                 methods.",
  acknowledgement = ack-nhfb,
  articleno =    "1950010",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Han:2019:RBV,
  author =       "Myounghee Han and Yongjoo Kim and Jang Ryul Park and
                 Benjamin J. Vakoc and Wang-Yuhl Oh and Sukyoung Ryu",
  title =        "Retinal Blood Vessel Caliber Estimation for Optical
                 Coherence Tomography Angiography Images Based on {$3$D}
                 Superellipsoid Modeling",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "2",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500116",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 10 09:47:18 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500116",
  abstract =     "Changes of retinal blood vessel calibers may reflect
                 various retinal diseases and even several non-retinal
                 diseases. We propose a new method to estimate retinal
                 vessel calibers from 3D optical coherence tomography
                 angiography (OCTA) images based on 3D modeling using
                 superellipsoids. Taking advantage of 3D visualization
                 of the retinal tissue microstructures in vivo provided
                 by OCTA, our method can detect retinal blood vessels
                 precisely, estimate their calibers reliably, and show
                 the relative flow speed visually.",
  acknowledgement = ack-nhfb,
  articleno =    "1950011",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Raja:2019:LPC,
  author =       "S. P. Raja",
  title =        "Line and Polygon Clipping Techniques on Natural Images
                 --- A Mathematical Solution and Performance
                 Evaluation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "2",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500128",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 10 09:47:18 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500128",
  abstract =     "The objective of this paper is to apply clipping
                 techniques on natural images and to analyze the
                 performance of various clipping algorithms in computer
                 graphics. The clipping techniques used in this paper is
                 Cohen--Sutherland line clipping, Liang--Barsky line
                 clipping, Nicholl--Lee--Nicholl line clipping and
                 Sutherland--Hodgman polygon clipping. The clipping
                 algorithms are evaluated by using the three parameters:
                 time complexity, space complexity and image accuracy.
                 Previously, there is no performance evaluation on
                 clipping algorithms done. Motivating by this factor, in
                 this paper an evaluation of clipping algorithms is
                 made. The novelty of this paper is to apply the
                 clipping algorithms on natural images. It is justified
                 that the above mentioned clipping algorithms outperform
                 well on clipping the natural images.",
  acknowledgement = ack-nhfb,
  articleno =    "1950012",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gill:2019:RES,
  author =       "Jasmeen Gill and Akshay Girdhar and Tejwant Singh",
  title =        "A Review of Enhancement and Segmentation Techniques
                 for Digital Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "3",
  pages =        "??--??",
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946781950013X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 23 06:58:38 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946781950013X",
  abstract =     "Image enhancement and segmentation are the two
                 imperative steps while processing digital images. The
                 goal of enhancement is to improve the quality of images
                 so as to nullify the effect of poor illumination
                 conditions during image acquisition. Afterwards,
                 segmentation is performed to extract region of interest
                 (ROI) from the background details of the image. There
                 is a vast literature available for both the techniques.
                 Therefore, this paper is intended to summarize the
                 basic as well as advanced enhancement and segmentation
                 techniques under a single heading; to provide an
                 insight for future researches in the field of pattern
                 recognition.",
  acknowledgement = ack-nhfb,
  articleno =    "1950013",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2019:IFB,
  author =       "Manoj Kumar and Anuj Rani and Sangeet Srivastava",
  title =        "Image Forensics Based on Lighting Estimation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "3",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500141",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 23 06:58:38 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500141",
  abstract =     "Computer generated images are assumed to be a key part
                 in each person's life in this era of information
                 technology, where individuals effectively inhabit the
                 advertisements, magazines, websites, televisions and
                 many more. At the point when digital images played
                 their role, the event of violations in terms of
                 misrepresentation of information, use of their wrong
                 doings winds up and also becomes easier with the help
                 of image editing application programs. To be
                 legitimate, if anyone does wrong anything then the
                 proposed method can be used for a correct
                 identification of the forgery and the imitations in the
                 digital images. In existing techniques, researchers
                 have suggested most well-known types of digital
                 photographic manipulations based on source, meta-data,
                 image copying, splicing and many more. The proposed
                 approach is inspired by physics-based techniques and
                 requires less human involvement. The presented approach
                 works for images having any type of objects present in
                 the scene, i.e. not only limited to human faces and
                 selection of same intensity regions of the image. By
                 assessing the lighting parameters, the proposed
                 technique identifies the manipulated object and returns
                 angle of incidence w.r.t light source direction. The
                 demonstrated result produces forgery recognition rate
                 of 92\% on an image dataset comprising of various types
                 of manipulated images.",
  acknowledgement = ack-nhfb,
  articleno =    "1950014",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Aditya:2019:ISF,
  author =       "B. P. Aditya and U. G. K. Avaneesh and K. Adithya and
                 Akshay Murthy and R. Sandeep and B. Kavyashree",
  title =        "Invisible Semi-Fragile Watermarking and Steganography
                 of Digital Videos for Content Authentication and Data
                 Hiding",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "3",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500153",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 23 06:58:38 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500153",
  abstract =     "In the current digital age, the piracy of digital
                 media content has become rampant. Illegal distribution
                 of movies and video clips on a global scale causes a
                 significant loss to the media industry. To prevent such
                 theft and distribution of content, we use a
                 watermarking technique for videos where copyright
                 information is hidden inside the original video in the
                 form of a watermark video. Using a video as the
                 watermark facilitates the user in hiding a large amount
                 of information. The watermarking scheme used in this
                 paper is semi-fragile, such that tampering of videos
                 can be detected with relative ease. To improve the
                 robustness of the watermark, we embed the watermark in
                 frequency domain, where we use DWT+DCT+SVD to embed the
                 watermark. The original video and watermark video are
                 transformed by using the DWT and DCT sequentially, then
                 the singular values of the watermark with some
                 embedding strength are added to the singular values of
                 the original video thus obtaining a watermarked video.
                 Some detection tools which are available today cannot
                 detect the watermark video inside the original video.
                 This method equalizes the frames of the watermark and
                 original video to reduce time consumed as well as
                 complexity. The effects of various attacks on the
                 watermarked video have been analyzed using the
                 calculated PSNR values.",
  acknowledgement = ack-nhfb,
  articleno =    "1950015",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Miyazaki:2019:EVD,
  author =       "Daisuke Miyazaki and Sayaka Taomoto and Shinsaku
                 Hiura",
  title =        "Extending the Visibility of Dichromats Using Histogram
                 Equalization of Hue Value Defined for Dichromats",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "3",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500165",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 23 06:58:38 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500165",
  abstract =     "Dichromats lack one of the three cone cells, which
                 detects red, green, and blue lights. For example,
                 red-green color blinds cannot distinguish the color
                 between red, yellow, and green. In order to extend the
                 ability of dichromats to recognize the color
                 difference, we proposed a method to expand the color
                 difference when observed by dichromats. We have defined
                 a hue variable for dichromats and implemented to our
                 algorithm. We applied the histogram equalization to the
                 hue of dichromats in order to enlarge the color
                 difference recognized by dichromats. We have applied
                 our method to RGB color image, and shown its
                 performance at the experimental section.",
  acknowledgement = ack-nhfb,
  articleno =    "1950016",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Cheng:2019:LDC,
  author =       "Lu Cheng and Yuan-Ke Zhang and Yun Song and Chen Li
                 and Dao-Shun Guo",
  title =        "Low-Dose {CT} Image Restoration Based on Adaptive
                 Prior Feature Matching and Nonlocal Means",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "3",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500177",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 23 06:58:38 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500177",
  abstract =     "Although the low-dose CT (LDCT) technique can reduce
                 the radiation damage to patients, it will be highly
                 detrimental to the reconstructed image quality. The
                 normal-dose scan assisted algorithms have shown their
                 potential in improving LDCT image quality by using a
                 registered previously scanned normal-dose CT (NDCT)
                 reference to regularize the corresponding LDCT target.
                 The major drawback of such methods is the requirement
                 of a previous patient-specific NDCT scan, which limits
                 their clinical application. To address these problems,
                 this paper proposed adaptive prior feature matching
                 method for better restoration of the LDCT image. The
                 innovation lies in construction of offline texture
                 feature database and online adaptive prior feature
                 matching integrated with the NLM regularization.
                 Specifically, the prior features were extracted by the
                 gray level co-occurrence matrix (GLCM) from regions of
                 interest (ROIs) in existing NDCT scans of population
                 patients. For online adaptive prior feature matching,
                 ROIs with their texture features being similar to those
                 of the current noisy target ROI are selected from the
                 database as the references for the NLM regularization.
                 The effectiveness of the proposed algorithm is
                 validated by clinical lung cancer studies, the gain
                 over traditional methods is noticeable in terms of both
                 noise suppression and textures preservation.",
  acknowledgement = ack-nhfb,
  articleno =    "1950017",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Veinidis:2019:EDH,
  author =       "Christos Veinidis and Antonios Danelakis and Ioannis
                 Pratikakis and Theoharis Theoharis",
  title =        "Effective Descriptors for Human Action Retrieval from
                 {$3$D} Mesh Sequences",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "3",
  pages =        "??--??",
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500189",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Jul 23 06:58:38 MDT 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500189",
  abstract =     "Two novel methods for fully unsupervised human action
                 retrieval using 3D mesh sequences are presented. The
                 first achieves high accuracy but is suitable for
                 sequences consisting of clean meshes, such as
                 artificial sequences or highly post-processed real
                 sequences, while the second one is robust and suitable
                 for noisy meshes, such as those that often result from
                 unprocessed scanning or 3D surface reconstruction
                 errors. The first method uses a spatio-temporal
                 descriptor based on the trajectories of 6 salient
                 points of the human body (i.e. the centroid, the top of
                 the head and the ends of the two upper and two lower
                 limbs) from which a set of kinematic features are
                 extracted. The resulting features are transformed using
                 the wavelet transformation in different scales and a
                 set of statistics are used to obtain the descriptor. An
                 important characteristic of this descriptor is that its
                 length is constant independent of the number of frames
                 in the sequence. The second descriptor consists of two
                 complementary sub-descriptors, one based on the
                 trajectory of the centroid of the human body across
                 frames and the other based on the Hybrid static shape
                 descriptor adapted for mesh sequences. The robustness
                 of the second descriptor derives from the robustness
                 involved in extracting the centroid and the Hybrid
                 sub-descriptors. Performance figures on publicly
                 available real and artificial datasets demonstrate our
                 accuracy and robustness claims and in most cases the
                 results outperform the state-of-the-art.",
  acknowledgement = ack-nhfb,
  articleno =    "1950018",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hamouda:2019:FAS,
  author =       "Maissa Hamouda and Karim Saheb Ettabaa and Med Salim
                 Bouhlel",
  title =        "Framework for Automatic Selection of Kernels based on
                 Convolutional Neural Networks and {CkMeans} Clustering
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467819500190",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500190",
  abstract =     "Convolutional neural networks (CNN) can learn deep
                 feature representation for hyperspectral imagery (HSI)
                 interpretation and attain excellent accuracy of
                 classification if we have many training samples. Due to
                 its superiority in feature representation, several
                 works focus on it, among which a reliable
                 classification approach based on CNN, used filters
                 generated from cluster framework, like k Means
                 algorithm, yielded good results. However, the kernels
                 number to be manually assigned. To solve this problem,
                 a HSI classification framework based on CNN, where the
                 convolutional filters to be adaptatively learned from
                 the data, by grouping without knowing the cluster
                 number, has recently proposed. This framework, based on
                 the two algorithms CNN and kMeans, showed high accuracy
                 results. So, in the same context, we propose an
                 architecture based on the depth convolutional neural
                 networks principle, where kernels are adaptatively
                 learned, using CkMeans network, to generate filters
                 without knowing the number of clusters, for
                 hyperspectral classification. With adaptive kernels,
                 the proposed framework automatic kernels selection by
                 CkMeans algorithm (AKSCCk) achieves a better
                 classification accuracy compared to the previous
                 frameworks. The experimental results show the
                 effectiveness and feasibility of AKSCCk approach.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Montazeri:2019:MAI,
  author =       "Mitra Montazeri",
  title =        "Memetic Algorithm Image Enhancement for Preserving
                 Mean Brightness Without Losing Image Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500207",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500207",
  abstract =     "In the image processing application, contrast
                 enhancement is a major step. Conventional contrast
                 enhancement methods such as Histogram Equalization (HE)
                 do not have satisfactory results on many different low
                 contrast images and they also cannot automatically
                 handle different images. These problems result in
                 specifying parameters manually to produce high contrast
                 images. In this paper, an automatic image contrast
                 enhancement on Memetic algorithm (MA) is proposed. In
                 this study, simple exploiter is proposed to improve the
                 current image contrast. The proposed method
                 accomplishes multi goals of preserving brightness,
                 retaining the shape features of the original histogram
                 and controlling excessive enhancement rate, suiting for
                 applications of consumer electronics. Simulation
                 results shows that in terms of visual assessment, peak
                 signal-to-noise (PSNR) and Absolute Mean Brightness
                 Error (AMBE) the proposed method is better than the
                 literature methods. It improves natural looking images
                 specifically in images with high dynamic range and the
                 output images were applicable for products of consumer
                 electronic.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2019:ISI,
  author =       "Uche A. Nnolim",
  title =        "Improved Single Image De-Hazing Via Sky Region
                 Detection, Classification and Illumination Refinement",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500219",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500219",
  abstract =     "This paper presents an automated sky detection
                 technique based on statistical and fuzzy rule-based
                 edge detection for improved hazy image contrast
                 enhancement. This is significant since most
                 conventional de-hazing approaches yield hazy images
                 with over-enhanced sky regions and under-enhanced
                 detail regions due to inability to adaptively determine
                 and enhance such regions. Earlier and current schemes
                 developed to remedy this issue are highly complex,
                 usually require training with vast amount of images and
                 manual tuning of one or several parameters. The
                 proposed method utilizes standard deviation and fuzzy
                 logic-based edge detection combined with thresholding
                 algorithms to generate a homogeneity map identifying
                 sky and non-sky regions. The areas of these regions are
                 subsequently computed and used to obtain a homogeneity
                 ratio. The ratio is then used to trigger a
                 decision-based, switching scheme incorporated into a
                 partial differential equation (PDE) de-hazing algorithm
                 to improve results. Alternatively, a log illumination
                 refinement method is proposed as a less complex
                 alternative combined with the modified PDE algorithm to
                 process hazy images without degrading sky regions,
                 while yielding brighter images. Several image datasets
                 from the literature were used to validate the proposed
                 approaches and yielded mostly consistent and comparable
                 results similar to or better than algorithms from the
                 literature.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Khadilkar:2019:FIB,
  author =       "Samrat P. Khadilkar and Sunil R. Das and Mansour H.
                 Assaf and Satyendra N. Biswas",
  title =        "Face Identification Based on Discrete Wavelet
                 Transform and Neural Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500220",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib;
                 https://www.math.utah.edu/pub/tex/bib/matlab.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500220",
  abstract =     "The subject paper presents implementation of a new
                 automatic face recognition system. To formulate an
                 automated framework for the recognition of human faces
                 is a highly challenging endeavor. The face
                 identification problem is particularly very crucial in
                 the context of today's rapid emergence of technological
                 advancements with ever expansive requirements. It has
                 also significant relevance in the related engineering
                 disciplines of computer graphics, pattern recognition,
                 psychology, image processing and artificial neural
                 networks. This paper proposes a side-view face
                 authentication approach based on discrete wavelet
                 transform and artificial neural networks for the
                 solution of the problem. A subset determination
                 strategy that expands on the number of training samples
                 and permits protection of the global information is
                 discussed. The authentication technique involves image
                 profile extraction, decomposition of the wavelets,
                 splitting of the subsets and finally neural network
                 verification. The procedure exploits the localization
                 property of the wavelets in both the frequency and
                 spatial domains, while maintaining the generalized
                 properties of the neural networks. The realization
                 strategy of the methodology was executed using MATLAB,
                 demonstrating that the performance of the technique is
                 quite satisfactory.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mashaly:2019:PAS,
  author =       "Ahmed S. Mashaly",
  title =        "Performance Assessment of Sky Segmentation Approaches
                 for {UAVs}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500232",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500232",
  abstract =     "Image segmentation is one of the most challenging
                 research fields for both image analysis and
                 interpretation. The applications of image segmentation
                 could be found as the primary step in various computer
                 vision systems. Therefore, the choice of a reliable and
                 accurate segmentation method represents a non-trivial
                 task. Since the selected image segmentation method
                 influences the overall performance of the remaining
                 system steps, sky segmentation appears as a vital step
                 for Unmanned Aerial Vehicle (UAV) autonomous obstacle
                 avoidance missions. In this paper, we are going to
                 introduce a comprehensive literature survey of the
                 different types of image segmentation methodology
                 followed by a detailed illustration of the
                 general-purpose methods and the state-of-art sky
                 segmentation approaches. In addition, we introduce an
                 improved version of our previously published work for
                 sky segmentation purpose. The performance of the
                 proposed sky segmentation approach is compared with
                 various image segmentation approaches using different
                 parameters and datasets. For performance assessment, we
                 test our approach under different situations and
                 compare its performance with commonly used approaches
                 in terms of several assessment indexes. From the
                 experimental results, the proposed method gives
                 promising results compared with the other image
                 segmentation approaches.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Suryanarayana:2019:SIS,
  author =       "Gunnam Suryanarayana and Ravindra Dhuli and Jie Yang",
  title =        "Single Image Super-Resolution Algorithm Possessing
                 Edge and Contrast Preservation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819500244",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819500244",
  abstract =     "In real time surveillance video applications, it is
                 often required to identify a region of interest in a
                 degraded low resolution (LR) image. State-of-the-art
                 super-resolution (SR) techniques produce images with
                 poor illumination and degraded high frequency details.
                 In this paper, we present a different approach for SISR
                 by correcting the dual-tree complex wavelet transform
                 (DT-CWT) subbands using the multi-stage cascaded joint
                 bilateral filter (MSCJBF) and singular value
                 decomposition (SVD). The proposed method exploits
                 geometric regularity for implementing the
                 covariance-based interpolation in the spatial domain.
                 We decompose the interpolated LR image into different
                 image and wavelet coefficients by employing DT-CWT. To
                 preserve edges, we alter the wavelet sub-bands with the
                 high frequency details obtained from the MSCJBF.
                 Simultaneously, we retain uniform illumination by
                 improving the image coefficients using SVD. In
                 addition, the wavelet sub-bands undergo Lanczos
                 interpolation prior to the subband refinement.
                 Experimental results demonstrate the effectiveness of
                 our method.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2019:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 19)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "19",
  number =       "4",
  pages =        "??--??",
  month =        oct,
  year =         "2019",
  DOI =          "https://doi.org/10.1142/S0219467819990018",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Feb 1 09:16:38 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467819990018",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nandal:2020:FOA,
  author =       "Savita Nandal and Sanjeev Kumar",
  title =        "Fractional-Order Anisotropic Diffusion for Defogging
                 of {RGB} Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467820500011",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 6 07:43:16 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500011",
  abstract =     "This paper proposes a novel and efficient algorithm
                 for defogging of color (RGB) images. The fog in a scene
                 is mostly due to the attenuation and airlight map,
                 which decrease the quality of the image of the scene.
                 To enhance such images from the visual point of view, a
                 fractional-order anisotropic diffusion algorithm with p
                 -Laplace norm is proposed for removing the fog effect.
                 In particular, a coupling term is added in order to
                 model the inter-channel correlations. The weights used
                 in the coupling term stop the transmission of diffusion
                 with in the edges, thus balances the inter-channel data
                 in the diffusion procedure. Experimental results
                 validate the better performance of the proposed
                 algorithm over some of the existing anisotropic
                 diffusion-based methods. The proposed method is
                 independent of the measure of fog in the images, thus
                 images with different amount of fog can be enhanced.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yadav:2020:EIA,
  author =       "Navneet Yadav and Navdeep Goel",
  title =        "An Effective Image-Adaptive Hybrid Watermarking Scheme
                 with Transform Coefficients",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500023",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 6 07:43:16 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500023",
  abstract =     "Robust and invisible watermarking provides a feasible
                 solution to prove the ownership of the genuine content
                 owner. Different watermarking algorithms have been
                 presented by the researchers in the past but no
                 algorithm could be termed as perfect. Proposed work
                 puts forward a novel image-adaptive method of embedding
                 a binary watermark in the image in a transparent
                 manner. Discrete wavelet transform (DWT), singular
                 value decomposition (SVD) and discrete cosine transform
                 (DCT) are used together in the proposed hybrid
                 watermarking scheme. Image-adaptive nature of the
                 scheme is reflected in the usage of only high entropy
                 8{\texttimes}8 blocks for the watermark embedding.
                 Binary watermark is embedded in the DCT coefficients
                 using a flexible strength derived from the means of the
                 DCT coefficients. This flexible strength factor (SF)
                 has different value for the DCT coefficients originated
                 from different 8{\texttimes}8 blocks. Any desired level
                 of visual quality could be obtained by varying the
                 adjusting parameter of the flexible SF. Side
                 information generated in the watermark embedding is
                 used in the detection of watermark. The presented
                 watermarking technique shows better robustness in
                 comparison to the three contemporary watermarking
                 techniques.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ye:2020:MFM,
  author =       "Dan Ye and Chiou-Shann Fuh",
  title =        "{$3$D} Morphable Face Model for Face Animation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500035",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 6 07:43:16 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500035",
  abstract =     "This paper employs a new technology for modeling
                 textured 3D faces. 3D faces can either be generated
                 automatically from one or more photographs, or modeled
                 directly through an intuitive user interface. Users are
                 assisted in two key problems of computer-aided face
                 modeling. It presents two algorithms for 3D face
                 modeling from an image sequence. The first method works
                 by creating an initial estimate using multiframe
                 structure from motion (SfM) reconstruction framework,
                 which is refined by comparing against a generic face
                 model. The comparison is carried out using an
                 energy-function optimization strategy. Results of 3D
                 reconstruction algorithm are presented. The second
                 method presented reconstructs a face model by adapting
                 a generic model to contours of a face over all the
                 frames of an image sequence. The algorithm for pose
                 estimation and 3D face reconstruction relies solely on
                 contours and the system does not require knowledge of
                 rendering parameters (e.g. light direction and
                 intensity). Results relying on finding accurate point
                 correspondences across frames is presented.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sakib:2020:RDB,
  author =       "Mohammad Nazmus Sakib and Shuvashis Das Gupta and
                 Satyendra N. Biswas",
  title =        "A Robust {DWT}-Based Compressed Domain Video
                 Watermarking Technique",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500047",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 6 07:43:16 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500047",
  abstract =     "To achieve robustness and imperceptibility, an
                 adaptive compressed domain blind video watermarking
                 method based on Discrete Wavelet Transform (DWT) is
                 proposed in this research. In this technique, multiple
                 binary images derived from a single watermark image are
                 first embedded in a video sequence. The spatial spread
                 spectrum watermark is directly incorporated in the
                 compressed bit streams by modifying the four sets of
                 discrete wavelet coefficients. Comprehensive simulation
                 experiments demonstrate that the developed approach is
                 efficient and also robust against spatial attacks such
                 as scaling and frame averaging, noise attacks such as
                 Gaussian and salt pepper noise, and temporal attacks
                 like frame dropping and shifting. Moreover, the
                 proposed approach can also withstand against rotation
                 attacks of arbitrary angle.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bibi:2020:SPP,
  author =       "Khalida Bibi and Ghazala Akram and Kashif Rehan",
  title =        "Shape Preserving Properties with Constraints on the
                 Tension Parameter of Binary Three-Point Approximating
                 Subdivision Scheme",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500059",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 6 07:43:16 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500059",
  abstract =     "The paper analyzes conditions for preserving the shape
                 properties from the initial data to the limit curves of
                 the binary three-point approximating subdivision
                 scheme. We provide suitable conditions on the initial
                 data utilizing the tension parameter $ \omega $, thus
                 the scheme can maintain three important shape
                 properties, namely positivity, monotonicity and
                 convexity in the limit curves. The use of derived
                 conditions is illustrated in few examples, which offers
                 more flexibility in the generation of smooth limit
                 curves endowed with shape preserving properties.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arora:2020:GFB,
  author =       "Tanvi Arora and Renu Dhir",
  title =        "Geometric Feature-Based Classification of Segmented
                 Human Chromosomes",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500060",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Feb 6 07:43:16 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500060",
  abstract =     "The chromosomes are the carriers of the geometric
                 information, any alteration in the structure or number
                 of these chromosomes is termed as genetic defect. These
                 alterations cause malfunctioning in the proteins and
                 are cause of the various underlying medical conditions
                 that are hard to cure or detect by normal clinical
                 procedures. In order to detect the underlying causes of
                 these defects, the cells of the humans need to be
                 imaged during the mitosis phase of cell division.
                 During this phase, the chromosomes are the longest and
                 can be easily studied and the alterations in the
                 structure and count of the chromosomes can be analyzed
                 easily. The chromosomes are non-rigid objects, due to
                 which they appear in varied orientations, which makes
                 them hard to be analyzed for the detection of
                 structural defects. In order to detect the genetic
                 abnormalities due to structural defects, the
                 chromosomes need to be in straight orientation.
                 Therefore, in this work, we propose to classify the
                 segmented chromosomes from the metaspread images into
                 straight, bent, touching overlapping or noise, so that
                 the bent, touching, overlapping chromosomes can be
                 preprocessed and straightened and the noisy objects be
                 discarded. The classification has been done using a set
                 of 17 different geometric features. We have proposed a
                 Multilayer Perceptron-based classification approach to
                 classify the chromosomes extracted from metaspread
                 images into five distinct categories considering their
                 orientation. The results of the classification have
                 been analyzed using the segmented objects of the
                 Advance Digital Imaging Research (ADIR) dataset. The
                 proposed technique is capable of classifying the
                 segmented chromosomes with 94.28\% accuracy. The
                 performance of the proposed technique has been compared
                 with seven other state-of-the-art classifiers and
                 superior results have been achieved by the proposed
                 method.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Girishwaingankar:2020:PNB,
  author =       "Poorva Girishwaingankar and Sangeeta Milind Joshi",
  title =        "The {PHY-NGSC}-Based {ORT} Run Length Encoding Scheme
                 for Video Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467820500072",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib;
                 https://www.math.utah.edu/pub/tex/bib/matlab.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500072",
  abstract =     "This paper proposes a compression algorithm using
                 octonary repetition tree (ORT) based on run length
                 encoding (RLE). Generally, RLE is one type of lossless
                 data compression method which has duplication problem
                 as a major issue due to the usage of code word or flag.
                 Hence, ORT is offered instead of using a flag or code
                 word to overcome this issue. This method gives better
                 performance by means of compression ratio, i.e.
                 99.75\%. But, the functioning of ORT is not good in
                 terms of compression speed. For that reason,
                 physical-next generation secure computing (PHY-NGSC) is
                 hybridized with ORT to raise the compression speed. It
                 uses an MPI-open MP programming paradigm on ORT to
                 improve the compression speed of encoder. The planned
                 work achieves multiple levels of parallelism within an
                 image such as MPI and OpenMP for parallelism across a
                 group of pictures level and slice level, respectively.
                 At the same time, wide range of data compression like
                 multimedia, executive files and documents are possible
                 in the proposed method. The performance of the proposed
                 work is compared with other methods like accordian RLE,
                 context adaptive variable length coding (CAVLC) and
                 context-based arithmetic coding (CBAC) through the
                 implementation in Matlab working platform.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Raja:2020:WBI,
  author =       "S. P. Raja",
  title =        "Wavelet-Based Image Compression Encoding Techniques
                 --- A Complete Performance Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500084",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500084",
  abstract =     "This paper presents a complete analysis of
                 wavelet-based image compression encoding techniques.
                 The techniques involved in this paper are embedded
                 zerotree wavelet (EZW), set partitioning in
                 hierarchical trees (SPIHT), wavelet difference
                 reduction (WDR), adaptively scanned wavelet difference
                 reduction (ASWDR), set partitioned embedded block coder
                 (SPECK), compression with reversible embedded wavelet
                 (CREW) and spatial orientation tree wavelet (STW).
                 Experiments are done by varying level of the
                 decomposition, bits per pixel and compression ratio.
                 The evaluation is done by taking parameters like peak
                 signal to noise ratio (PSNR), mean square error (MSE),
                 image quality index (IQI) and structural similarity
                 index (SSIM), average difference (AD), normalized
                 cross-correlation (NK), structural content (SC),
                 maximum difference (MD), Laplacian mean squared error
                 (LMSE) and normalized absolute error (NAE).",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Soni:2020:TRE,
  author =       "Rituraj Soni and Bijendra Kumar and Satish Chand",
  title =        "Text Region Extraction From Scene Images Using {AGF}
                 and {MSER}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500096",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500096",
  abstract =     "The natural scene images contain text as an integral
                 part of that image that supplies paramount knowledge
                 about it. This information and knowledge can be used in
                 the variety of purposes like image-based searching,
                 automatic number plate recognition, robot navigation,
                 etc. but text region extraction and detection in
                 scenery images could be quite a challenging job due to
                 image blur, distortion, noise, etc. In this paper, we
                 discuss a method for extraction of text regions by
                 generating prospective components by applying maximally
                 stable extremal regions (MSER) and boundary smoothing
                 by Alternating guided image filter, which is one of the
                 newest filters to deal with noise and halo effect
                 elimination. The separation of non-text \& text
                 components is achieved by AdaBoost classifier that
                 separates text and non-text on the basis of the three
                 text specific features namely maximum stroke width
                 ratio, compactness, color divergence. The proposed
                 method assist in extracting text regions from the
                 blurred and low contrast natural scene images
                 effectively. The ICDAR 2013 training and testing
                 dataset is applied for the experiments and evaluation
                 of the method. The evaluation is carried out using
                 deteval software for calculating precision, f-measure,
                 recall for the detected, and extracted text regions.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2020:PMS,
  author =       "Uche A. Nnolim",
  title =        "Probabilistic, Multi-Scale Fractional Tonal Correction
                 Bilateral Filter-Based Hazy Image Enhancement",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500102",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500102",
  abstract =     "This paper describes an algorithm utilizing a modified
                 multi-scale fractional order-based operator combined
                 with a probabilistic tonal operator, adaptive color
                 enhancement and bilateral filtering to process hazy and
                 underwater images. The multi-scale algorithm
                 complements the tonal operator by enhancing edges,
                 preventing overexposure of bright image regions, while
                 enhancing details in the dark areas. The addition of a
                 previously developed global enhancement operator
                 removes color cast and improves global contrast in
                 underwater images. The color enhancement function
                 augments the color results of the dehazing algorithm
                 without distorting image intensity. Furthermore, the
                 bilateral filter suppresses noise while preserving
                 enhanced details/edges due to the multi-scale
                 algorithm. Experimental results indicate that the
                 proposed system yields comparable or better results
                 than other algorithms from the literature.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Surajkanta:2020:RIA,
  author =       "Yumnam Surajkanta and Shyamosree Pal",
  title =        "Recognition of Isothetic Arc Using Number Theoretic
                 Properties",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500114",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500114",
  abstract =     "In this paper, we propose an arc recognition method
                 based on the number theoretic properties of isothetic
                 covers. A definition of digital circles is given based
                 on the dilation of Euclidean circles with unit squares.
                 We show that a variant of the digital circle is
                 equivalent to the grid centers between the isothetic
                 covers of disks. Number theoretic properties of the
                 isothetic covers of disks are explored and show that
                 the distribution of square numbers can be used to find
                 the run lengths of the isothetic covers. Arc
                 recognition algorithms are developed based on the
                 number theoretic properties.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fuchsberger:2020:SAT,
  author =       "Alexander Fuchsberger and Brian Ricks and Zhicheng
                 Chen",
  title =        "A Semi-Automated Technique for Transcribing Accurate
                 Crowd Motions",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500126",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500126",
  abstract =     "We present a novel technique for transcribing crowds
                 in video scenes that allows extracting the positions of
                 moving objects in video frames. The technique can be
                 used as a more precise alternative to image processing
                 methods, such as background-removal or automated
                 pedestrian detection based on feature extraction and
                 classification. By manually projecting pedestrian
                 actors on a two-dimensional plane and translating
                 screen coordinates to absolute real-world positions
                 using the cross ratio, we provide highly accurate and
                 complete results at the cost of increased processing
                 time. We are able to completely avoid most errors found
                 in other automated annotation techniques, resulting
                 from sources such as noise, occlusion, shadows, view
                 angle or the density of pedestrians. It is further
                 possible to process scenes that are difficult or
                 impossible to transcribe by automated image processing
                 methods, such as low-contrast or low-light
                 environments. We validate our model by comparing it to
                 the results of both background-removal and feature
                 extraction and classification in a variety of scenes.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Omari:2020:CPA,
  author =       "Mohammed Omari and Yamina Ouled Jaafri and Rekia
                 Dlim",
  title =        "Comparative Performance Analysis of Enhancement
                 Methods Applied to {Arabic} Manuscripts",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500138",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500138",
  abstract =     "The ancient Arabic manuscripts are considered to be
                 more complex regarding enhancement compared to others
                 written in other languages. Complexity comes from
                 character degradation, stains, low-quality images,
                 curves of the text, character overlapping, etc. To
                 facilitate the restoration, a set of well-known
                 binarization techniques designed for historical
                 document images is presented in this paper. Existing
                 binarization techniques focus on either finding an
                 appropriate global threshold for the whole image or
                 adapting a local threshold for each area to achieve
                 better enhancement quality. This improvement aims to
                 remove noises, strains, uneven illumination, etc. The
                 goal of our work is to assess these methods when
                 applied to Arabic manuscripts in terms of readability,
                 elimination of original spots and production of
                 unwanted noise. Results show that no techniques work
                 well for all types of manuscripts, but some techniques
                 work better than others for particular types.
                 Experimental results also indicate that Nick and Wolf
                 techniques performed the best in terms of readability
                 in most of the processed manuscripts.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arunlal:2020:DIC,
  author =       "S. L. Arunlal and N. Santhi and K. Ramar",
  title =        "Design and Implementation of Content-Based Natural
                 Image Retrieval Approach Using Feature Distance",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S021946782050014X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782050014X",
  abstract =     "Generally, the database is a gathering of data that is
                 arranged for simple storage, retrieval and modernize.
                 This data comprises of numerous structures like text,
                 table, and image, outline and chart and so on.
                 Content-based image retrieval (CBIR) is valuable for
                 calculating the huge amount of image databases and
                 records and for distinguishes retrieving similar
                 images. Rather than text-based searching, CBIR
                 effectively recovers images that are similar like query
                 image. CBIR assumes a significant role in various areas
                 including restorative finding, industry estimation,
                 geographical information satellite frameworks (GIS
                 frameworks), and biometrics; online searching and
                 authentic research, etc. Here different medical
                 database images are considered to the CBIR procedure is
                 done by the proposed strategy. The proposed method
                 considers the input features are shape, texture
                 feature, wavelet feature, and SIFT feature. To retrieve
                 the input image based on the features, the suggested
                 method utilizes artificial neural network (ANN)
                 structure. Back-propagation technique, which is an
                 organizational structure for learning is utilized for
                 training the neural network framework. Trial
                 demonstrates that the proposed work improves the
                 results of the retrieval system. From the outcomes
                 minimizes the image retrieval time and maximum
                 Precision 87.3\% in distance based ANN process.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sinduja:2020:EET,
  author =       "A. Sinduja and A. Suruliandi and S. P. Raja",
  title =        "Empirical Evaluation of Texture Features and
                 Classifiers for Liver Disease Diagnosis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500151",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500151",
  abstract =     "The liver cancer is one of the most common fatal
                 diseases worldwide, and its early detection through
                 medical imaging is a major contributor to the reduction
                 in mortality from certain cancer. This paves the way to
                 work on diagnosing liver diseases effectively. An
                 accurate diagnosis of liver disease in CT image
                 requires an efficient description of textures and
                 classification methods. This paper performs comparative
                 analysis on proposed texture feature descriptor with
                 the different existing texture features with various
                 classifiers to classify six types of diffused and focal
                 liver diseases. The classification of liver diseases is
                 done in two stages. In first stage, features like
                 segmentation based fractal texture analysis, counting
                 label occurrence matrix, local configuration pattern,
                 eXtended center-symmetric local binary pattern and the
                 proposed local symmetric tetra pattern are used for
                 extracting information from the CT liver structure and
                 classifiers like support vector machine, k -nearest
                 neighbor, and naive Bayes are used for classifying the
                 pathologic liver. When pathologic conditions are
                 detected, the best feature descriptors and classifiers
                 are used to classify the results into any of six
                 exclusive pathologic liver diseases, in second stage.
                 The experiments are carried out in medically validated
                 liver datasets containing normal and six-disease
                 category of liver. The first experiment is analyzed
                 using sensitivity, specificity, and accuracy. The
                 second experiment is evaluated using precision, recall,
                 BCR, and F-measure. The results demonstrate that the
                 local symmetric tetra pattern with k -nearest neighbor
                 classifier culminates in a state-of-the-art performance
                 for diagnosing liver diseases.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2020:FFB,
  author =       "Sandeep Kumar and L. Suresh",
  title =        "Fruit Fly-Based Artificial Neural Network Classifier
                 with Kernel-Based Fuzzy $c$-Means Clustering for
                 Satellite Image Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500163",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon May 11 09:44:18 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500163",
  abstract =     "Image segmentation and classification are the major
                 challenges to satellite imagery. Also, the
                 identification of unique objects in the satellite image
                 is a significant aspect in the application of remote
                 sensing. Many satellite image classification techniques
                 have been presented earlier. However, the accuracy of
                 the image classification has to be further improved. So
                 that, optimal artificial neural network with
                 kernel-based fuzzy c-means ( KFCM+OANN ) clustering
                 based satellite image classification is proposed in
                 this paper. Initially, the images are segmented with
                 the help of KFCM algorithm. Then, color features and
                 gray level co-occurrence matrix (GLCM) features to be
                 extracted from the segmented regions. Then, these
                 extracted features are given to the OANN classifier.
                 Based on these features, segmented regions are
                 classified as building, road, shadow, and tree. To
                 enhance the performance of the classifier, the weight
                 values are optimally selected with the help of fruit
                 fly algorithm. Simulation results show that the
                 performance of proposed classifier outperforms that of
                 the existing filters in terms of accuracy.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Divakara:2020:NDI,
  author =       "S. S. Divakara and Sudarshan Patilkulkarni and Cyril
                 Prasanna Raj",
  title =        "Novel {DWT\slash IDWT} Architecture for {$3$D} with
                 Nine Stage {$2$D} Parallel Processing using Split
                 Distributed Arithmetic",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467820500175",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500175",
  abstract =     "Novel high-speed memory optimized distributed
                 arithmetic (DA)-based architecture is developed and
                 modeled for 3D discrete wavelet transform (DWT). The
                 memory requirement for the proposed architecture is
                 designed to 9{\texttimes}9N+36 pixel dynamic memory
                 space and 52W ROM. The proposed 3D-DWT architecture
                 implements 9/7 Daubechies wavelet filters, synthesizes
                 7127 bytes of memory for temporary storage and uses 758
                 adders, 36 multiplexers of 16:1 and 36 up counter to
                 realize the 3D-DWT hardware. The 3D-DWT engine is
                 implemented and tested in a Xilinx FPGA Vertex5
                 XC5VLX155T with high area and power efficiency. The
                 maximum delay in the timing path is 2.676 ns and the
                 3D-DWT works at maximum frequency of 381 MHz clock.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shrivastava:2020:ASR,
  author =       "Neeraj Shrivastava and Jyoti Bharti",
  title =        "Automatic Seeded Region Growing Image Segmentation for
                 Medical Image Segmentation: a Brief Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500187",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500187",
  abstract =     "In the domain of computer technology, image processing
                 strategies have become a part of various applications.
                 A few broadly used image segmentation methods have been
                 characterized as seeded region growing (SRG),
                 edge-based image segmentation, fuzzy k -means image
                 segmentation, etc. SRG is a quick, strongly formed and
                 impressive image segmentation algorithm. In this paper,
                 we delve into different applications of SRG and their
                 analysis. SRG delivers better results in analysis of
                 magnetic resonance images, brain image, breast images,
                 etc. On the other hand, it has some limitations as
                 well. For example, the seed points have to be selected
                 manually and this manual selection of seed points at
                 the time of segmentation brings about wrong selection
                 of regions. So, a review of some automatic seed
                 selection methods with their advantages, disadvantages
                 and applications in different fields has been
                 presented.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Savakar:2020:ERU,
  author =       "Dayanand G. Savakar and Ravi Hosur",
  title =        "The {$3$D} Emotion Recognition Using {SVM} and {HoG}
                 Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500199",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500199",
  abstract =     "Emotion recognition is becoming commercially popular
                 due to the major role of analytics in various aspects
                 of marketing and strategy management. Several papers
                 have been proposed in emotion recognition. They are
                 mainly classified in the past under 2D and 3D emotion
                 recognition, out of which 2D emotion recognition has
                 been more popular. Various aspects like facial posture,
                 light intensity variations and sensor-independent
                 recognition have been studied by different authors in
                 the past. However, in reality, 3D emotion recognition
                 has been found to be more efficient which has a broader
                 area of use. In this paper, a 3D tracking plane with 2D
                 feature points has enabled us to recognize emotions by
                 statistical voting method from all planes having over
                 threshold number of points in their respective contour
                 area. The proposed technique's results are comparable
                 to existing methods in terms of time, space complexity
                 and accuracy improvement.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Salehi:2020:UID,
  author =       "Hadi Salehi and Javad Vahidi",
  title =        "An Ultrasound Image Despeckling Method Based on
                 Weighted Adaptive Bilateral Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500205",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500205",
  abstract =     "Images are widely used in engineering. Unfortunately,
                 ultrasound images are mainly degraded by an intrinsic
                 noise called speckle. Therefore, de-speckling is a
                 critical preprocessing step. Therefore, a robust
                 despeckling method and accurate evaluation of images
                 are suggested. We suggest three phases and a three-step
                 denoising filter. In the first phase, the coefficients
                 of variation are computed from the noisy image. The
                 second phase is a three-step denoising filter. The
                 first step is denoising of extreme levels of
                 homogeneous regions, based on fuzzy homogeneous
                 regions. The second step is a proposed adaptive
                 bilateral filter (ABF). The ABF helps for better
                 denoising based on the three regions which are edge,
                 detail and homogeneous regions. The next step, a
                 weight, is applied to the ABF. This step is for
                 isolated noise denoising. Next, in the third phase, the
                 output image is evaluated by the fuzzy logic approach.
                 The proposed method is compared with other filters in
                 the literature. The experimental outcomes show that the
                 proposed method has better performance than the other
                 filters. That proposed denoising algorithm is able to
                 preserve image details and edges when compared with
                 other denoising methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nikesh:2020:DVB,
  author =       "P. Nikesh and G. Raju",
  title =        "Directional Vector-Based Skin Lesion Segmentation ---
                 a Novel Approach to Skin Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500217",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500217",
  abstract =     "Efficient skin lesion segmentation algorithms are
                 required for computer aided diagnosis of skin cancer.
                 Several algorithms were proposed for skin lesion
                 segmentation. The existing algorithms are short of
                 achieving ideal performance. In this paper, a novel
                 semi-automatic segmentation algorithm is proposed. The
                 fare concept of the proposed is 8-directional search
                 based on threshold for lesion pixel, starting from a
                 user provided seed point. The proposed approach is
                 tested on 200 images from PH2 and 900 images from ISBI
                 2016 datasets. In comparison to a chosen set of
                 algorithms, the proposed approach gives high accuracy
                 and specificity values. A significant advantage of the
                 proposed method is the ability to deal with
                 discontinuities in the lesion.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Debnath:2020:UAS,
  author =       "Saswati Debnath and Pinki Roy",
  title =        "User Authentication System Based on Speech and Cascade
                 Hybrid Facial Feature",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500229",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500229",
  abstract =     "With the increasing demand for security in many
                 fastest growing applications, biometric recognition is
                 the most prominent authentication system. User
                 authentication through speech and face recognition is
                 the important biometric technique to enhance the
                 security. This paper proposes a speech and facial
                 feature-based multi-modal biometric recognition
                 technique to improve the authentication of any system.
                 Mel Frequency Cepstral Coefficients (MFCC) is extracted
                 from audio as speech features. In visual recognition,
                 this paper proposes cascade hybrid facial (visual)
                 feature extraction method based on static, dynamic and
                 key-point salient features of the face and it proves
                 that the proposed feature extraction method is more
                 efficient than the existing method. In this proposed
                 method, Viola--Jones algorithm is used to detect static
                 and dynamic features of eye, nose, lip, Scale Invariant
                 Feature Transform (SIFT) algorithm is used to detect
                 some stable key-point features of face. In this paper,
                 a research on the audio-visual integration method using
                 AND logic is also made. Furthermore, all the
                 experiments are carried out using Artificial Neural
                 Network (ANN) and Support Vector Machine (SVM). An
                 accuracy of 94.90\% is achieved using proposed feature
                 extraction method. The main objective of this work is
                 to improve the authenticity of any application using
                 multi-modal biometric features. Adding facial features
                 to the speech recognition improve system security
                 because biometric features are unique and combining
                 evidence from two modalities increases the authenticity
                 as well as integrity of the system.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prashar:2020:NCA,
  author =       "Navdeep Prashar and Meenakshi Sood and Shruti Jain",
  title =        "Novel Cardiac Arrhythmia Processing using Machine
                 Learning Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500230",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500230",
  abstract =     "Electrocardiography (ECG) signals provides assistance
                 to the cardiologists for identification of various
                 cardiovascular diseases (CVD). ECG machine records the
                 electrical activity of the heart with the assistance of
                 electrodes placed on the patient's body. Qualitative
                 characterization of ECG signal reflects its
                 sensitiveness towards distinct artifacts that resulted
                 in low diagnostic accuracy and may lead to incorrect
                 decision of the clinician. The artifacts are removed
                 utilizing a robust noise estimator employing DTCWT
                 using various threshold values and functions. The
                 segments and intervals of ECG signals are calculated
                 using the peak detection algorithm followed by particle
                 swarm optimization (PSO) and the proposed optimization
                 technique to select the best features from a
                 considerable pool of features. Out of the 12 features,
                 the best four features are selected using PSO and the
                 proposed optimization technique. Comparative analysis
                 with other feature selection methods and
                 state-of-the-art techniques demonstrated that the
                 proposed algorithm precisely selects principle features
                 for handling the ECG signal and attains better
                 classification utilizing distinctive machine learning
                 algorithms. The obtained accuracy using our proposed
                 optimization technique is 95.71\% employing k -NN and
                 neural networks. Also, 4\% and 10\% improvements have
                 been observed while using k -NN over ANN and SVM,
                 respectively, when the PSO technique is executed.
                 Similarly, a 14.16\% improvement is achieved while
                 using k -NN and ANN over the SVM machine learning
                 technique for the proposed optimization technique.
                 Heart rate is calculated using the proposed estimator
                 and optimization technique, which is in consensus with
                 the gold standard.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jiji:2020:DST,
  author =       "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai
                 Raj",
  title =        "Decision Support Techniques for Dermatology Using
                 Case-Based Reasoning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500242",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500242",
  abstract =     "Identification of skin disease has become a
                 challenging task with the origination of various skin
                 diseases. This paper presents a case-based reasoning
                 (CBR) decision support system to enhance dermatological
                 diagnosis for rural and remote communities. In this
                 proposed work, an automated way is introduced to deal
                 with the inconsistency problem in CBRs. This new hybrid
                 architecture is to support the diagnosis in multiple
                 skin diseases. The architecture used case-based
                 reasoning terminology facilitates the medical
                 diagnosis. Case based reasoning system retrieves the
                 data which contains symptoms and treatment plan of the
                 disease from the data repository by the way of matching
                 visual contents of the image, such as shape, texture,
                 and color descriptors. The extracted feature vector is
                 fed into a framework to retrieve the data. The results
                 proved using ROC curve that the proposed architecture
                 yields high contribution to the computer-aided
                 diagnosis of skin lesions. In experimental analysis,
                 the system yields a specificity of 95.25\% and a
                 sensitivity of 86.77\%. Our empirical evaluation has a
                 superior retrieval and diagnosis performance when
                 compared to the performance of other works.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nisha:2020:STP,
  author =       "S. Shajun Nisha and S. P. Raja and A. Kasthuri",
  title =        "Static Thresholded Pulse Coupled Neural Networks in
                 Contourlet Domain --- a New Framework for Medical Image
                 Denoising",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500254",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500254",
  abstract =     "Image denoising, a significant research area in the
                 field of medical image processing, makes an effort to
                 recover the original image from its noise corrupted
                 image. The Pulse Coupled Neural Networks (PCNN) works
                 well against denoising a noisy image. Generally, image
                 denoising techniques are directly applied on the
                 pixels. From the literature review, it is reported that
                 denoising after frequency domain transformation is
                 performing better since noise removal is applied over
                 the coefficients. Motivated by this, in this paper, a
                 new technique called the Static Thresholded Pulse
                 Coupled Neural Network (ST-PCNN) is proposed by
                 combining PCNN with traditional filtering or threshold
                 shrinkage technique in Contourlet Transform domain.
                 Four different existing PCNN architectures, such as
                 Neuromime Structure, Intersecting Cortical Model,
                 Unit-Linking Model and Multichannel Model are
                 considered for comparative analysis. The filters such
                 as Wiener, Median, Average, Gaussian and threshold
                 shrinkage techniques such as Sure Shrink, HeurShrink,
                 Neigh Shrink, BayesShrink are used. For noise removal,
                 a mixture of Speckle and Gaussian noise is considered
                 for a CT skull image. A mixture of Rician and Gaussian
                 noise is considered for MRI brain image. A mixture of
                 Speckle and Salt and Pepper noise is considered for a
                 Mammogram image. The Performance Metrics such as Peak
                 Signal-to-Noise Ratio (PSNR), Structural Similarity
                 Index (SSIM), Image Quality Index (IQI), Universal
                 Image Quality Index (UQI), Image Enhancement Filter
                 (IEF), Structural Content (SC), Correlation Coefficient
                 (CC), and Weighted Signal-to-Noise Ratio (WSNR) and
                 Visual Signal-to-Noise Ratio (VSNR) are used to
                 evaluate the performance of denoising.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Araujo:2020:ECQ,
  author =       "Leonardo C. Araujo and Joao P. H. Sansao and Mario C.
                 S. Junior",
  title =        "Effects of Color Quantization on {JPEG} Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500266",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:10 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500266",
  abstract =     "This paper analyzes the effects of color quantization
                 on standard JPEG compression. Optimized color palettes
                 were used to quantize natural images, using dithering
                 and chroma subsampling as optional. The resulting
                 variations on file size and quantitative quality
                 measures were analyzed. Preliminary results, using a
                 small image database, show that file size suffered an
                 average 20\% increase and a concomitant loss in quality
                 was perceived ( {\textminus} 6dB PSNR, {\textminus}
                 0.16 SSIM and {\textminus} 9.6 Butteraugli). Color
                 quantization present itself as an ineffective tool on
                 JPEG compression but if necessarily imposed, on high
                 quality compressed images, it might lead to a
                 negligible increase in data size and quality loss. In
                 addition dithering seems to always decrease JPEG
                 compression ratio.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{MacIel:2020:SOF,
  author =       "Luiz Maur{\'\i}lio {da Silvad Maciel} and Marcelo
                 Bernardes Vieira",
  title =        "Sparse Optical Flow Computation Using Wave
                 Equation-Based Energy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467820500278",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500278",
  abstract =     "Identification of motion in videos is a fundamental
                 task for several computer vision problems. One of the
                 main tools for motion identification is optical flow,
                 which estimates the projection of the 3D velocity of
                 the objects onto the plane of the camera. In this work,
                 we propose a differential optical flow method based on
                 the wave equation. The optical flow is computed by
                 minimizing a functional energy composed by two terms: a
                 data term based on brightness constancy and a
                 regularization term based on energy of the wave. Flow
                 is determined by solving a system of linear equations.
                 The decoupling of the pixels in the solution allows
                 solving the system by a direct or iterative approach
                 and makes the method suitable for parallelization. We
                 present the convergence conditions for our method since
                 it does not converge for all the image points. For
                 comparison purposes, we create a global video
                 descriptor based on histograms of optical flow for the
                 problem of action recognition. Despite its sparsity,
                 results show that our method improves the average
                 motion estimation, compared with classical methods. We
                 also evaluate optical flow error measures in image
                 sequences of a classical dataset for method
                 comparison.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mondal:2020:COD,
  author =       "Ajoy Mondal",
  title =        "Camouflaged Object Detection and Tracking: a Survey",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S021946782050028X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782050028X",
  abstract =     "Moving object detection and tracking have various
                 applications, including surveillance, anomaly
                 detection, vehicle navigation, etc. The literature on
                 object detection and tracking is rich enough, and there
                 exist several essential survey papers. However, the
                 research on camouflage object detection and tracking is
                 limited due to the complexity of the problem. Existing
                 work on this problem has been done based on either
                 biological characteristics of the camouflaged objects
                 or computer vision techniques. In this paper, we review
                 the existing camouflaged object detection and tracking
                 techniques using computer vision algorithms from the
                 theoretical point of view. This paper also addresses
                 several issues of interest as well as future research
                 direction in this area. We hope this paper will help
                 the reader to learn the recent advances in camouflaged
                 object detection and tracking.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Brahme:2020:EVV,
  author =       "Aparna Brahme and Umesh Bhadade",
  title =        "Effect of Various Visual Speech Units on Language
                 Identification Using Visual Speech Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500291",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500291",
  abstract =     "In this paper, we describe our work in Spoken language
                 Identification using Visual Speech Recognition (VSR)
                 and analyze the effect of various visual speech units
                 used to transcribe the visual speech on language
                 recognition. We have proposed a new approach of word
                 recognition followed by the word N-gram language model
                 (WRWLM), which uses high-level syntactic features and
                 the word bigram language model for language
                 discrimination. Also, as opposed to the traditional
                 visemic approach, we propose a holistic approach of
                 using the signature of a whole word, referred to as a
                 ``Visual Word'' as visual speech unit for transcribing
                 visual speech. The result shows Word Recognition Rate
                 (WRR) of 88\% and Language Recognition Rate (LRR) of
                 94\% in speaker dependent cases and 58\% WRR and 77\%
                 LRR in speaker independent cases for English and
                 Marathi digit classification task. The proposed
                 approach is also evaluated for continuous speech input.
                 The result shows that the Spoken Language
                 Identification rate of 50\% is possible even though the
                 WRR using Visual Speech Recognition is below 10\%,
                 using only 1s of speech. Also, there is an improvement
                 of about 5\% in language discrimination as compared to
                 traditional visemic approaches.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nair:2020:RCA,
  author =       "Arun T. Nair and K. Muthuvel",
  title =        "Research Contributions with Algorithmic Comparison on
                 the Diagnosis of Diabetic Retinopathy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500308",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500308",
  abstract =     "The medical field has been revolutionized by the
                 medical imaging system, which plays a key role in
                 providing information on the early life-saving
                 detection of dreadful diseases. Diabetic retinopathy is
                 a chronic visual disease that is the primary reason for
                 the vision loss in most of the patients, who left
                 undiagnosed at the initial stage. As the count of the
                 diabetic retinopathy affected people kept on
                 increasing, there is a necessity to have an automated
                 detection method. The accuracy of the diagnosis of the
                 automatic detection model is related to image
                 acquisition as well as image interpretation. In
                 contrast to this, the analysis of medical images by
                 using computerized models is still a limited task.
                 Thus, different kinds of detection methods are being
                 developed for early detection of diabetic retinopathy.
                 Accordingly, this paper focuses on the various
                 literature analyses on different detection algorithms
                 and techniques for diagnosing diabetic retinopathy.
                 Here, it reviews several research papers and exhibits
                 the significance of each detection method. This review
                 deals with the analysis on the segmentation as well as
                 classification algorithms that are included in each of
                 the researches. Besides, the adopted environment,
                 database collection and the tool for each of the
                 research are portrayed. It provides the details of the
                 performance analysis of the various diabetic detection
                 models and reveals the best value in the case of each
                 performance measure. Finally, it widens the research
                 issues that can be accomplished by future researchers
                 in the detection of diabetic retinopathy.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rukundo:2020:NEP,
  author =       "Olivier Rukundo",
  title =        "Non-Extra Pixel Interpolation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S021946782050031X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782050031X",
  abstract =     "A non-extra pixel interpolation NPI is introduced for
                 efficient image upscaling purposes. The NPI algorithm
                 uses extended-triangular and linear scaling functions
                 to match the pixel coordinates. The triangular function
                 uses a modulo-operator with only two variables
                 representing image pixels and scaling ratio. Every two
                 variables of the linear scaling function represent the
                 source/destination image pixels and scaling ratio. The
                 traditional ceil function is used to round off
                 non-integer pixel coordinates. The {\em circshift\/}
                 and {\em padarray\/} functions are used to circularly
                 shift the elements in array output by $k$-amount in
                 each dimension and pad elements of the $d$-th {\em
                 columns/rows\/} by {\em g-padsize\/} in the shifted
                 array, respectively. The $k$, $d$ and $g$ values are
                 determined with respect to integer scaling ratios by a
                 vector of $n$-elements. The Exactness, Peak
                 Signal-to-Noise Ratio, Signal-to-Noise Ratio and
                 Discrete Fourier Transform techniques were used for
                 objective evaluation purposes. Experiments demonstrated
                 comparable results as well as the need for further
                 researches.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bania:2020:ATM,
  author =       "Rubul Kumar Bania and Anindya Halder",
  title =        "Adaptive Trimmed Median Filter for Impulse Noise
                 Detection and Removal with an Application to Mammogram
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500321",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500321",
  abstract =     "Mammography imaging is one of the most widely used
                 techniques for breast cancer screening and analysis of
                 abnormalities. However, due to some technical
                 difficulties during the time of acquisition and digital
                 storage of mammogram images, impulse noise may be
                 present. Therefore, detection and removals of impulse
                 noise from the mammogram images are very essential for
                 early detection and further diagnosis of breast cancer.
                 In this paper, a novel {\em adaptive trimmed median
                 filter\/} (ATMF) is proposed for impulse noise (salt &
                 pepper (SNP)) detection and removal with an application
                 to mammogram image denoising. Automatic switching
                 mechanism for updating the {\em Window of Interest\/}
                 (WoI) size from ( 3{\texttimes}3 ) to ( 5{\texttimes}5
                 ) or ( 7{\texttimes}7 ) is performed. The proposed
                 method is applied on publicly available mammogram
                 images corrupted with varying SNP noise densities in
                 the range 5\%--90\%. The performance of the proposed
                 method is measured by various quantitative indices like
                 {\em peak signal to noise ratio\/} (PSNR), {\em mean
                 square error\/} (MSE), {\em image enhancement factor\/}
                 (IEF) and {\em structural similarity index measure\/}
                 (SSIM). The comparative analysis of the proposed method
                 is done with respect to other state-of-the-art noise
                 removal methods viz., {\em standard median filter\/}
                 (SMF), {\em decision based median filter\/} (DMF), {\em
                 decision based unsymmetric trimmed median filter\/}
                 (DUTMF), {\em modified decision based unsymmetric
                 trimmed median filter\/} (MDUTMF) and {\em decision
                 based unsymmetric trimmed winsorized mean filter\/}
                 (DUTWMF). The superiority of the proposed method over
                 other compared methods is well evident from the
                 experimental results in terms of the quantitative
                 indices (viz., PSNR, IEF and SSIM) and also from the
                 visual quality of the denoised images. Paired {\em
                 t-test\/} confirms the statistical significance of the
                 higher PSNR values achieved by the proposed method as
                 compared to the other counterpart techniques. The
                 proposed method turned out to be very effective in
                 denoising both high and low density noises present in
                 (mammogram) images.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kiley:2020:WMF,
  author =       "Matthew R. Kiley and Md Shafaeat Hossain",
  title =        "Who are My Family Members? {A} Solution Based on Image
                 Processing and Machine Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500333",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500333",
  abstract =     "Image creation and retention are growing at an
                 exponential rate. Individuals produce more images today
                 than ever in history and often these images contain
                 family. In this paper, we develop a framework to detect
                 or identify family in a face image dataset. The ability
                 to identify family in a dataset of images could have a
                 critical impact on finding lost and vulnerable
                 children, identifying terror suspects, social media
                 interactions, and other practical applications. We
                 evaluated our framework by performing experiments on
                 two facial image datasets, the Y-Face and KinFaceW,
                 comprising 37 and 920 images, respectively. We tested
                 two feature extraction techniques, namely PCA and HOG,
                 and three machine learning algorithms, namely {\em K\/}
                 -Means, agglomerative hierarchical clustering, and {\em
                 K\/} nearest neighbors. We achieved promising results
                 with a maximum detection rate of 94.59\% using {\em
                 K\/} -Means, 89.18\% with agglomerative clustering, and
                 77.42\% using {\em K\/} -nearest neighbors.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jiji:2020:FSE,
  author =       "G. Wiselin Jiji and A. Rajesh",
  title =        "Food Sustenance Estimation Using Food Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500345",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500345",
  abstract =     "The upcoming generation is at high risk of developing
                 many health issues like heart diseases, metabolic
                 diseases and other life-threatening problems with high
                 mortality as a consequence of obesity due to intake of
                 unhealthy food which is totally deviated from a normal
                 balanced diet with appropriate calories, proteins,
                 vitamins and carbohydrates. In this work, the nutrient
                 intake is calculated using food image. Our system
                 provides efficient segmentation algorithms for
                 separating food items from the plate. The given 2D
                 image of food is converted into 3D image by generating
                 its depth map for volume generation and color, texture
                 and shape features are extracted. These features are
                 fed as input into multi-class support vector machine
                 classifier for learning. The learning phase involves
                 training of various mixed and non mixed food items. The
                 testing phase includes query image segmentation and
                 classification for identifying the type of food and
                 then finding calories using the nutrition data table.
                 We have also estimated the ingredient and decay of food
                 items. Our result shows accurate calorie estimation for
                 various kinds of food items.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dhariwal:2020:INW,
  author =       "Sumit Dhariwal and Sellappan Palaniappan",
  title =        "Image Normalization and Weighted Classification Using
                 an Efficient Approach for {SVM} Classifiers",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500357",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500357",
  abstract =     "The content of massive image changing the brightest
                 brightness is an impasse between most tests of sorted
                 image realizations with low-resolution representation.
                 I have done this research through image security, which
                 will help curb crime in the coming days, and we propose
                 a novel receipt for their strong and effective
                 counterpart. Image classification using low levels of
                 the image is a difficult method, so for this, I have
                 adopted the method of automating the semantic image
                 classification of this research and used it with
                 different SVM classifiers, based on the normalized
                 weighted feature support vector machine for semantic
                 image classification. This is a novel approach given
                 that weighted feature or normalized biased feature is
                 applied and it is found that the normalized method is
                 the best. It also uses normalized weighted features to
                 compute kernel functions and train SVM. The trained SVM
                 is then used to classify new images. During training
                 and generalization, we displayed a decrease of
                 identification error rate and there have been many
                 benefits of using SVM with better performance in
                 normalized image-cataloging systems. The importance of
                 this technique and its role will be highlighted in the
                 years to come.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tripathi:2020:SAH,
  author =       "Kirthi Tripathi and Harsh Sohal and Shruti Jain",
  title =        "Statistical Analysis of {HRV} Parameters for the
                 Detection of Arrhythmia",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820500369",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820500369",
  abstract =     "The repolarization and depolarization in heart
                 generate electrical signals in the form of an ECG wave.
                 The condition of the heart can be indicated by using
                 Heart Rate Variability (HRV) features. In this work,
                 FIR filter is used at the pre-processing phase for
                 denoising, and then statistical analysis is applied for
                 time-domain HRV feature extraction and selection. This
                 algorithm is evaluated on different records of MIT/BIH
                 Normal Sinus Rhythm and Arrhythmia database. The t
                 -test implementation in both databases shows that there
                 are significant variations in HRV features, where
                 meanRR and HR have suggestive significant ( {0.05$<$
                 p}{\textlessequal}0.10 ) changes, while maxRR, minRR,
                 maxminRR, and SDNN have strongly significant (
                 p{\textlessequal}0.01 ) changes. To validate the
                 statistical analysis of HRV, feature classification has
                 been done using SVM and kNN classifiers. A significant
                 improvement of 2\% and 14.02\% has been observed in the
                 overall accuracy of SVM and kNN classifiers after
                 feature selection, respectively. These HRV features can
                 be used for the early prediction of various
                 Cardio-Vascular Diseases (CVD).",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2020:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 20)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "20",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2020",
  DOI =          "https://doi.org/10.1142/S0219467820990016",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467820990016",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zaghloul:2021:FSI,
  author =       "Rawan I. Zaghloul and Hazem Hiary",
  title =        "A Fast Single Image Fog Removal Method Using Geometric
                 Mean Histogram Equalization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467821500017",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500017",
  abstract =     "Fog is a natural phenomenon that affects scene
                 visibility, it reduces the contrast of the image and
                 causes color-fade. While various works in the
                 literature have addressed this issue, a fast effective
                 model is still lacking. In this paper, a single image
                 fog removal based on Geometric Mean Histogram
                 Equalization (GMHE) is proposed. In particular, the
                 proposed method is composed of three steps. The primary
                 step is to adaptively tune the performance of GMHE
                 according to the properties of the color histogram of
                 the foggy image. The obtained result then enters two
                 levels of chromaticity enhancement using the Hue
                 Saturation Value (HSV) and rotors color
                 transformations, respectively. Extensive experiments
                 demonstrate that the proposed method attains high
                 performance compared to the state-of-the-art methods in
                 terms of quality and execution time. The evaluation is
                 performed qualitatively by visual assessment, and
                 quantitatively using a set of full reference and
                 no-reference-based measures. As well, we suggest an
                 assessment criterion to combine the results of the
                 standard measures in a single score to facilitate the
                 comparisons between the different fog removal
                 methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ravikumar:2021:PPB,
  author =       "K. P. Ravikumar and H. S. Manjunatha Reddy",
  title =        "Pixel Prediction-Based Image Steganography Using Crow
                 Search Algorithm-Based Deep Belief Network Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500029",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500029",
  abstract =     "Securing the confidentiality of patient information
                 using the image steganography process has gained more
                 attention in the research community. However, embedding
                 the patient information is a major task in the
                 steganography process due to the complexity in
                 identifying the pixel features. Thus, an effective Crow
                 Search Algorithm-based deep belief network (CSA-DBN) is
                 proposed for embedding the information in the medical
                 image. Initially, the appropriate pixels and the
                 features, like pixel coverage, wavelet energy, edge
                 information, and texture features, such as local binary
                 pattern (LBP) and local directional pattern (LDP), are
                 extracted from each pixel. The proposed CSA-DBN
                 utilizes the feature vector and identifies the suitable
                 pixels used for embedding. The patient information is
                 embedded into the image by using the embedding strength
                 and the DWT coefficient. Finally, the embedded
                 information is extracted using the DWT coefficient. The
                 analysis of the proposed CSA-DBN approach is done based
                 on the performance metrics, such as correlation
                 coefficient, peak signal-to-noise ratio (PSNR), and
                 structural similarity index (SSIM) that acquired the
                 average values as 0.9471, 24.836 dB, and 0.4916 in the
                 presence of salt and pepper noise and 0.9741, 57.832
                 dB, and 0.9766 in the absence of noise.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jagdale:2021:MRO,
  author =       "Rohita H. Jagdale and Sanjeevani K. Shah",
  title =        "Modified Rider Optimization-Based {V} Channel
                 Magnification for Enhanced Video Super Resolution",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500030",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500030",
  abstract =     "In video Super Resolution (SR), the problem of cost
                 expense concerning the attainment of enhanced spatial
                 resolution, computational complexity and difficulties
                 in motion blur makes video SR a complex task. Moreover,
                 maintaining temporal consistency is crucial to
                 achieving an efficient and robust video SR model. This
                 paper plans to develop an intelligent SR model for
                 video frames. Initially, the video frames in RGB format
                 will be transformed into HSV. In general, the
                 improvement in video frames is done in V-channel to
                 achieve High-Resolution (HR) videos. In order to
                 enhance the RGB pixels, the current window size is
                 enhanced to high-dimensional window size. As a novelty,
                 this paper intends to formulate a high-dimensional
                 matrix with enriched pixel intensity in V-channel to
                 produce enhanced HR video frames. Estimating the
                 enriched pixels in the high-dimensional matrix is
                 complex, however in this paper, it is dealt in a
                 significant way by means of a certain process: (i)
                 motion estimation (ii) cubic spline interpolation and
                 deblurring or sharpening. As the main contribution, the
                 cubic spline interpolation process is enhanced via
                 optimization in terms of selecting the optimal
                 resolution factor and different cubic spline
                 parameters. For optimal tuning, this paper introduces a
                 new modified algorithm, which is the modification of
                 the Rider Optimization Algorithm (ROA) named Mean
                 Fitness-ROA (MF-ROA). Once the HR image is attained, it
                 combines the HSV and converts to RGB, which obtains the
                 enhanced output RGB video frame. Finally, the
                 performance of the proposed work is compared over other
                 state-of-the-art models with respect to BRISQUE, SDME
                 and ESSIM measures, and proves its superiority over
                 other models.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hassan:2021:CEM,
  author =       "Mohd Fikree Hassan",
  title =        "Color Enhancement Method to Improve the Colors of the
                 Images Perceived by the Elderly People",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500042",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500042",
  abstract =     "Smartphones and tablets present data and information
                 in color images. Due to factors such as yellowing
                 pigmentation and miosis filter, elderly people may
                 experience difficulties and confusion when looking at
                 the color images on smartphones and tablets. In this
                 paper, we propose a color enhancement method to improve
                 the color perceived by elderly people. This method is
                 based on the color perception of the elderly simulated
                 using the uniform yellowing pigmentation method. The
                 proposed method enhances the colors of the images to
                 compensate for the effect of yellowing pigmentation and
                 miosis filter. This is achieved by utilizing the error
                 parameters between the original colors and colors
                 perceived by the elderly. Implementing an adaptation
                 matrix, the error parameters are modified and
                 distributed back into the original colors iteratively.
                 Experimental results showed that the proposed method
                 improves the colors perceived by the elderly.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jiji:2021:CRF,
  author =       "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai
                 Raj",
  title =        "{CBI + R}: a Fusion Approach to Assist Dermatological
                 Diagnoses",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500054",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500054",
  abstract =     "With the emerge of advanced technologies such as
                 high-resolution cameras and computational power, it
                 seems to ease to built a better dermatological
                 diagnostic system. However, the identification of skin
                 disease is still a challenging problem with the
                 origination of various skin diseases. In this paper, we
                 proposed a new fusion architecture --- CBI + R to
                 support the diagnosis in multiple skin diseases. The
                 architecture combines Content-Based Image Retrieval
                 (CBIR) and Case-Based Reasoning (CBR) technology
                 together to facilitate medical diagnosis. CBIR is to
                 retrieve digital dermoscopy images from a data
                 repository using the shape, texture and color features.
                 Along with these features, CBR is incorporated which
                 contains symptoms, case history and treatment plan of
                 the disease. Experiments on a set of 1210 images
                 yielded an accuracy of 98.2\%. This was a superior
                 retrieval and diagnosis performance in comparison with
                 the state-of-the-art works.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Joshi:2021:SDI,
  author =       "Anand B. Joshi and Dhanesh Kumar and D. C. Mishra",
  title =        "Security of Digital Images Based on {$3$D} {Arnold}
                 Cat Map and Elliptic Curve",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500066",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500066",
  abstract =     "Security of digital data is an important task in the
                 present era. In this paper, we propose a new scheme of
                 digital image encryption and decryption method based on
                 three-dimensional (3D) Arnold cat map (ACM) and
                 elliptic curve. In this proposed encryption method, we
                 have applied 3D ACM on the digital color image which
                 performs the dual encryption first, it performs the
                 permutation and second, it performs the substitution of
                 image pixels. After that, elliptic curve cryptography
                 (ECC) is used to encrypt the image, for this a mapping
                 method is proposed to convert the pixels of the image
                 as points on the elliptic curve. Further, a mapping
                 inverting method is proposed for decryption and then 3D
                 inverse Arnold cat map (iACM) is applied to get the
                 original image. The statistical and security analyses
                 are done on various images and the experimental results
                 show the robustness of the proposed method.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Akrour:2021:FHI,
  author =       "Leila Akrour and Soltane Ameur and Mourad Lahdir and
                 R{\'e}gis Fournier and Amine Nait Ali",
  title =        "Fast Hyperspectral Image Encoder Based on Supervised
                 Multimodal Scheme",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500078",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500078",
  abstract =     "Many compression methods, lossy or lossless, were
                 developed for 3D hyperspectral images, and various
                 standards have emerged and applied to these amounts of
                 data in order to achieve the best rate-distortion
                 performance. However, high-dimensional data volume of
                 hyperspectal images is problematic for compression and
                 decompression time. Nowadays, fast compression and
                 especially fast decompression algorithms are of primary
                 importance in image data applications. In this case, we
                 present a lossy hyperspectral image compression based
                 on supervised multimodal scheme in order to improve the
                 compression results. The supervised multimodal method
                 is used to reduce the amount of data before their
                 compression with the 3D-SPIHT encoder based on 3D
                 wavelet transform. The performance of the Supervised
                 Multimodal Compression (SMC-3D-SPIHT encoder) has been
                 evaluated on AVIRIS hyperspectral images. Experimental
                 results indicate that the proposed algorithm provides
                 very promising performance at low bit-rates while
                 reducing the encoding/decoding time.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Suryanarayana:2021:SRB,
  author =       "Gunnam Suryanarayana and Kandala N. V. P. S. Rajesh
                 and Jie Yang",
  title =        "Super-Resolution Based on Residual Learning and
                 Optimized Phase Stretch Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S021946782150008X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782150008X",
  abstract =     "High resolution infrared (IR) images are often
                 required in military and industrial applications. Due
                 to the limited properties of IR imaging sensors and
                 camera lens, IR images exhibit poor spatial resolution
                 with a blur phenomenon in the edge regions. In this
                 correspondence, we develop a new super-resolution
                 (SR)-IR image reconstruction method using the residual
                 learning network in the wavelet domain (WRESNET) and
                 optimized phase stretch transform (PST). Our algorithm
                 first transforms the input low resolution (LR)-IR image
                 into its low-frequency and high-frequency subbands
                 using the discrete wavelet decomposition. Subsequently,
                 we introduce the optimized PST to operate on the LR-IR
                 image and extract the intrinsic edge structure. The PST
                 behaves differently at low-frequency and high-frequency
                 regions, thus capturing the intensity variations for
                 edge detection. We incorporate the PST extracted edge
                 map in the wavelet subbands to preserve the intrinsic
                 structure of images. The resultant subbands are further
                 refined based on the missing residuals obtained using
                 the WRESNET. The proposed method is validated through
                 quantitative and qualitative evaluations against the
                 conventional and state-of-art SR methods. Results
                 reveal that the proposed method outperforms the
                 existing methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pradhan:2021:ALE,
  author =       "Ashis Pradhan and Mohan P. Pradhan",
  title =        "Automatic Localization of Elevation Values in a Poor
                 Quality Topographic Map",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500091",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500091",
  abstract =     "A topographic sheet hosts various morphological
                 features that effectively describe the terrain. This
                 multi-faced information content not only elevates human
                 perception but also provides ample direction for
                 research initiatives. Out of all possible attributes
                 based on utility, contours have wide set of
                 application. A contour is characterized by its
                 coordinate system and most importantly, its elevation
                 detail. Upon, successful attainment of these two
                 attributes, creating a fully automatic 3D projection
                 system may be achieved with relative ease. In contrast
                 to the traditional manual approach, this research
                 initiative puts forward a novel mechanism for
                 automatically localizing contour and its attributes
                 including coordinate pattern and elevation value in a
                 referenced map. To accomplish the aforementioned
                 objectives, the proposed mechanism relies on various
                 image processing techniques based on morphological
                 operations. Further, the extracted details can be used
                 to project the contours in a 3D space. This projection
                 is also called Digital Elevation Model (DEM). DEM is
                 crucial for various applications such as Terrain
                 Modeling, Hydrological Modeling, Path Optimization, to
                 name a few. Automatically and accurately created DEM
                 from topographic sheet could contribute a lot in many
                 Geographical Information System (GIS) applications.
                 This paper focuses mainly on elevation value
                 localization associated with specific contour.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Silva:2021:CMA,
  author =       "Rodrigo Dalvit C. Silva and Thomas R. Jenkyn",
  title =        "Classification of Mammogram Abnormalities Using
                 {Legendre} Moments",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500108",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500108",
  abstract =     "In this paper, the issue of classifying mammogram
                 abnormalities using images from an mammogram image
                 analysis society (MIAS) database is discussed. We
                 compare a feature extractor based on Legendre moments
                 (LMs) with six other feature extractors. To determine
                 the best feature extractor, the performance of each was
                 compared in terms of classification accuracy rate and
                 extraction time using a k -nearest neighbors ( k -NN)
                 classifier. This study shows that feature extraction
                 using LMs performed best with an accuracy rate over
                 84\% and requiring relatively little time for feature
                 extraction, on average only 1 s.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jiji:2021:DPD,
  author =       "G. Wiselin Jiji and A. Rajesh and P. Johnson Durai
                 Raj",
  title =        "Diagnosis of {Parkinson}'s Disease Using {SVM}
                 Classifier",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946782150011X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782150011X",
  abstract =     "Parkinson's disease (PD) is the most common disease
                 that affects aged people which leads to
                 dopamine-producing cells in substantia nigra to be
                 damaged when motor system degenerates. Clinical
                 Diagnosis of Parkinson's disease at the earlier stage
                 is very difficult. This work is carried out to find the
                 significance of cognition function of basal ganglia
                 (BG) region and speech data values. The BG can be
                 segmented using morphological operation and active
                 contour algorithm. Co-occurrences features are
                 extracted and out of 720 features, the promising 110
                 features are selected using variance method. More
                 promising 22 features are selected in speech data and
                 both features are individually classified using SVM to
                 find out the efficiency in Diagnosis. The outcome shows
                 cognition function of BG performing a major role in
                 early diagnosis of Parkinson's disease when compared to
                 speech data.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Boudraa:2021:ECS,
  author =       "Omar Boudraa and Walid Khaled Hidouci and Dominique
                 Michelucci",
  title =        "An Efficient Cooperative Smearing Technique for
                 Degraded Historical Document Image Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500121",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500121",
  abstract =     "Segmentation is one of the critical steps in
                 historical document image analysis systems that
                 determines the quality of the search, understanding,
                 recognition and interpretation processes. It allows
                 isolating the objects to be considered and separating
                 the regions of interest (paragraphs, lines, words and
                 characters) from other entities (figures, graphs,
                 tables, etc.). This stage follows the thresholding,
                 which aims to improve the quality of the document and
                 to extract its background from its foreground, also for
                 detecting and correcting the skew that leads to redress
                 the document. Here, a hybrid method is proposed in
                 order to locate words and characters in both
                 handwritten and printed documents. Numerical results
                 prove the robustness and the high precision of our
                 approach applied on old degraded document images over
                 four common datasets, in which the pair (Recall,
                 Precision) reaches approximately 97.7\% and 97.9\%.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Khosravi:2021:NIR,
  author =       "Javanshir Khosravi and Mohammad Shams Esfand Abadi and
                 Reza Ebrahimpour",
  title =        "A Novel Iterative Rigid Image Registration Algorithm
                 Based on the {Newton} Method",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500133",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500133",
  abstract =     "In recent years, Image Registration has attracted lots
                 of attention due to its capabilities and numerous
                 applications. Various methods have been exploited to
                 map two images with the same concept but different
                 conditions. Considering the finding of the mentioned
                 map as an optimization problem, mathematical-based
                 optimization methods have been extensively employed due
                 to their real-time performances. In this paper, we
                 employed the Newton method to optimize two defined cost
                 functions. These cost functions are Sum of Square
                 Difference and Cross-Correlation. These presented
                 algorithms have fast convergence and accurate features.
                 Also, we propose an innovative treatment in order to
                 attend to one of the free parameter-rotations or scale
                 as a sole variable and the other one as the constant
                 value. The assignment is replaced through the
                 iterations for both parameters. The intuition is to
                 turn a two-variable optimization problem into a single
                 variable one in every step. Our simulation on benchmark
                 images by the means of Root Mean Square Error and
                 Mutual Information as the goodness criteria, that have
                 been extensively used in similar studies, has shown the
                 robustness and affectivity of the proposed method.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Poonkuntran:2021:SIC,
  author =       "S. Poonkuntran and P. Alli and T. M. Senthil Ganesan
                 and S. Manthira Moorthi and M. P. Oza",
  title =        "Satellite Image Classification Using Cellular
                 Automata",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500145",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500145",
  abstract =     "The satellite image classification plays a vital role
                 in remote sensing for analyzing the images and
                 recognizing the patterns. Supervised classification is
                 one of the methods in which pixels of an image are
                 grouped based on training samples. The uncertainty is
                 one of the major issues in a supervised classification,
                 where the pixel is classified into more than one class.
                 This is happened due to the use of spectral values
                 without considering contextual values in
                 classification. Hence, this paper proposes Cellular
                 Automata (CA)-based Classifier for Satellite Images
                 Classification, where spectral values are combined with
                 contextual values to improve the accuracy of the
                 classifier. The proposed CA classifier combines the
                 spectral values with contextual values in iteration
                 until the uncertainty is resolved. Thereby, the
                 proposed scheme improves the accuracy of the classical
                 supervised classifier of parallel piped, minimum
                 distance, DSVM and KNN classifier by 7\% at an
                 average.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Siddiqui:2021:CBV,
  author =       "Tanveer J. Siddiqui and Ashish Khare",
  title =        "Chaos-based Video Steganography Method in Discrete
                 Cosine Transform Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500157",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500157",
  abstract =     "Due to the technological advancements in digital
                 communication, the amount of multimedia content over
                 the internet has increased manifold in past decade.
                 This has renewed the internet of researchers in the
                 area of privacy and secure communication. This paper
                 presents a secure and robust video steganography method
                 in discrete cosine transform (DCT) domain. In order to
                 enhance the security of the proposed algorithm, the
                 frame selection process is randomized and the secret
                 data are pre-treated using Arnold's cat map. The secret
                 data are embedded in the middle band DCT coefficient
                 using two pseudo random sequences. These sequences are
                 generated using a chaotic map. We analyze the proposed
                 algorithm in terms of peak signal-to-noise ratio
                 (PSNR), structural similarity index (SSIM), multi-scale
                 structural similarity index (MSSIM) and video quality
                 metric (VQM). The evaluation has been done on 107 video
                 sequences. The experimental results demonstrate that
                 the algorithm maintains acceptable video quality. The
                 robustness of the proposed method is tested under
                 Gaussian and salt and pepper noise attack using
                 correlation between original and recovered images. The
                 proposed algorithm is able to recover 90.60\% data
                 without error under salt and pepper noise ( D=0.001 )
                 attack and 87.23\% data correctly under Gaussian noise
                 attack with mean $ = 0 $ and variance $ = 0.001 $.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jameel:2021:MLT,
  author =       "Samer Kais Jameel and Sezgin Aydin and Nebras H.
                 Ghaeb",
  title =        "Machine Learning Techniques for Corneal Diseases
                 Diagnosis: a Survey",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500169",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500169",
  abstract =     "Machine learning techniques become more related to
                 medical researches by using medical images as a
                 dataset. It is categorized and analyzed for ultimate
                 effectiveness in diagnosis or decision-making for
                 diseases. Machine learning techniques have been
                 exploited in numerous researches related to corneal
                 diseases, contribution to ophthalmologists for
                 diagnosing the diseases and comprehending the way
                 automated learning techniques act. Nevertheless,
                 confusion still exists in the type of data used,
                 whether it is images, data extracted from images or
                 clinical data, the course reliant on the type of device
                 for obtaining them. In this study, the researches that
                 used machine learning were reviewed and classified in
                 terms of the kind of utilized machine for capturing
                 data, along with the latest updates in sophisticated
                 approaches for corneal disease diagnostic techniques.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Budhiraja:2021:IVI,
  author =       "Sumit Budhiraja and Iftisam Rummy and Sunil Agrawal
                 and Balwinder Singh Sohi",
  title =        "Infrared and Visible Image Fusion Based on Sparse
                 Representation and Spatial Frequency in {DTCWT}
                 Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500170",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500170",
  abstract =     "Infrared and visible image fusion is a key area of
                 research in multi-sensor image fusion. The main purpose
                 of this fusion is to combine thermal information of the
                 infrared image and texture information of the visible
                 image. This paper presents an image fusion framework,
                 based on parallel arrangement of sparse representation
                 (SR) and spatial frequency (SF). In the proposed
                 framework, an efficient edge-aware filter, i.e. guided
                 filter, is first employed on the visible image. Then
                 dual-tree complex wavelet transform (DTCWT) is used to
                 obtain low-pass and high-pass coefficients of images,
                 as it is shift-invariant and has high directional
                 selectivity. The low-pass coefficients are fused using
                 the SR- and SF-based fusion rules in parallel, which
                 enhances the regional features of the images. The
                 simulation results show that the proposed technique has
                 better performance when compared with conventional
                 techniques in both subjective and objective
                 evaluations.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Choudhary:2021:MBB,
  author =       "Swati K. Choudhary and Ameya K. Naik",
  title =        "Multimodal Biometric-Based Authentication with Secured
                 Templates",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500182",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500182",
  abstract =     "This paper proposes a multimodal biometric based
                 authentication (verification and identification) with
                 secured templates. Multimodal biometric systems provide
                 improved authentication rate over unimodal systems at
                 the cost of increased concern for memory requirement
                 and template security. The proposed framework performs
                 person authentication using face and fingerprint.
                 Biometric templates are protected by hiding fingerprint
                 into face at secret locations, through blind and
                 key-based watermarking. Face features are extracted
                 from approximation sub-band of Discrete Wavelet
                 Transform, which reduces the overall working plane. The
                 proposed method also shows high robustness of biometric
                 templates against common channel attacks. Verification
                 and identification performances are evaluated using two
                 chimeric and one real multimodal dataset. The same
                 systems, working with compressed templates provides
                 considerable reduction in overall memory requirement
                 with negligible loss of authentication accuracies.
                 Thus, the proposed framework offers positive balance
                 between authentication performance, template robustness
                 and memory resource utilization.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pathade:2021:SMC,
  author =       "Manasi Pathade and Madhuri Khambete",
  title =        "Supervised Method for Congestion Detection at Entry
                 and Exit Corridors of Public Places",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500194",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500194",
  abstract =     "Continuous monitoring and automatic detection of crowd
                 activities is extremely helpful for management at
                 public places to avoid any possible disaster. Analysis
                 of crowded scene is a critical task as it typically
                 involves poor resolution of objects, occlusions and
                 complex dynamics. In this paper, we propose a novel,
                 systematic and generalized method based on global
                 motion analysis of people to detect Congestion
                 situation in crowded scenes at entry/exit corridors.
                 Our approach is tested on video footages acquired from
                 surveillance cameras installed at exit corridors of
                 public places. The results show the expediency of our
                 approach.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Manchanda:2021:ICB,
  author =       "Meenu Manchanda and Deepak Gambhir",
  title =        "Improvement in {CNN}-Based Multifocus Image Fusion
                 Algorithm with Triangulated Fuzzy Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500200",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500200",
  abstract =     "Multifocus image fusion is a demanding research field
                 due to the utilization of modern imaging devices.
                 Generally, the scene to be captured contains objects at
                 different distances from these devices and so a set of
                 multifocus images of the scene is captured with
                 different objects in-focus. However, to improve the
                 situational awareness of the captured scene, these sets
                 of images are required to be fused together. Therefore,
                 a multifocus image fusion algorithm based on
                 Convolutional Neural Network (CNN) and triangulated
                 fuzzy filter is proposed. A CNN is used to extract
                 information regarding focused pixels of input images
                 and the same is used as fusion rule for fusing the
                 input images. The focused information so extracted may
                 still need to be refined near the boundaries.
                 Therefore, asymmetrical triangular fuzzy filter with
                 the median center (ATMED) is employed to correctly
                 classify the pixels near the boundary. The advantage of
                 using this filter is to rely on precise detection
                 results since any misdetection may considerably degrade
                 the fusion quality. The performance of the proposed
                 algorithm is compared with the state-of-art image
                 fusion algorithms, both subjectively and objectively.
                 Various parameters such as edge strength ( Q ), fusion
                 loss (FL), fusion artifacts (FA), entropy ( H ),
                 standard deviation (SD), spatial frequency (SF),
                 structural similarity index measure (SSIM) and feature
                 similarity index measure (FSIM) are used to evaluate
                 the performance of the proposed algorithm. Experimental
                 results proved that the proposed fusion algorithm
                 produces a fused image that contains all-in-one focused
                 pixels and is better than those obtained using other
                 popular and latest image fusion works.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sharma:2021:RIB,
  author =       "Urvashi Sharma and Meenakshi Sood and Emjee
                 Puthooran",
  title =        "Region of Interest-Based Coding Technique of Medical
                 Images Using Varying Grading of Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500212",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500212",
  abstract =     "A region of interest (ROI)-based compression method
                 for medical image datasets is a requirement to maintain
                 the quality of the diagnostically important region of
                 the image. It is always a better option to compress the
                 diagnostic important region in a lossless manner and
                 the remaining portion of the image with a near-lossless
                 compression method to achieve high compression
                 efficiency without any compromise of quality. The
                 predictive ROI-based compression on volumetric CT
                 medical image is proposed in this paper;
                 resolution-independent gradient edge detection (RIGED)
                 and block adaptive arithmetic encoding (BAAE) are
                 employed to ROI part for prediction and encoding that
                 reduce the interpixel and coding redundancy. For the
                 non-ROI portion, RIGED with an optimal threshold value,
                 quantizer with optimal q -level and BAAE with optimal
                 block size are utilized for compression. The volumetric
                 8-bit and 16-bit standard CT image dataset is utilized
                 for the evaluation of the proposed technique, and
                 results are validated on real-time CT images collected
                 from the hospital. Performance of the proposed
                 technique in terms of BPP outperforms existing
                 techniques such as JPEG 2000, M-CALIC, JPEG-LS, CALIC
                 and JP3D by 20.31\%, 19.87\%, 17.77\%, 15.58\% and
                 13.66\%, respectively.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Panwar:2021:FES,
  author =       "Kirtee Panwar and Ravindra Kumar Purwar and Garima
                 Srivastava",
  title =        "A Fast Encryption Scheme Suitable for Video
                 Surveillance Applications Using {SHA-256} Hash Function
                 and {$1$D} Sine--Sine Chaotic Map",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500224",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500224",
  abstract =     "This paper proposes an image encryption technique
                 which is fast and secure. The encryption scheme is
                 designed for secure transmission of video surveillance
                 data (keyframes) over insecure network. The image
                 encryption technique employs 1D Sine--Sine system with
                 better chaotic properties than its seed map and faster
                 than higher-dimensional chaotic systems. Further,
                 design of encryption scheme is based on two permutation
                 rounds, which employs pixel swapping operation and
                 diffusion operation which is simple and provides
                 required security against plaintext, differential and
                 various other attacks. Three separate chaotic sequences
                 are generated using 1D Sine--Sine system which enhances
                 the key space of the encryption scheme. Secret keys are
                 updated dynamically with SHA-256 hash value obtained
                 from plain image. Hash values of plain image are
                 efficiently used without loss of any hash value
                 information. This makes the encryption scheme plaintext
                 sensitive and secure against plaintext attacks.
                 Performance and security aspects of encryption scheme
                 is analyzed both quantitatively using predefined
                 security metrics and qualitatively by scrutinizing the
                 internal working of encryption scheme. Computational
                 complexity of encrypting a plain image of size \(
                 rows{\texttimes} columns \) is {$ \mathcal {O} $} \(
                 rows{\texttimes}columns \) and is suitable for
                 encrypting keyframes of video for secure surveillance
                 applications.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kamath:2021:DSI,
  author =       "Priya R. Kamath and Kedarnath Senapati and P. Jidesh",
  title =        "Despeckling of {SAR} Images Using Shrinkage of
                 Two-Dimensional Discrete Orthonormal {$S$}-Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500236",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500236",
  abstract =     "Speckles are inherent to SAR. They hide and undermine
                 several relevant information contained in the SAR
                 images. In this paper, a despeckling algorithm using
                 the shrinkage of two-dimensional discrete orthonormal
                 S-transform (2D-DOST) coefficients in the transform
                 domain along with shock filter is proposed. Also, an
                 attempt has been made as a post-processing step to
                 preserve the edges and other details while removing the
                 speckle. The proposed strategy involves decomposing the
                 SAR image into low and high-frequency components and
                 processing them separately. A shock filter is used to
                 smooth out the small variations in low-frequency
                 components, and the high-frequency components are
                 treated with a shrinkage of 2D-DOST coefficients. The
                 edges, for enhancement, are detected using a
                 ratio-based edge detection algorithm. The proposed
                 method is tested, verified, and compared with some
                 well-known models on C-band and X-band SAR images. A
                 detailed experimental analysis is illustrated.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kalaivani:2021:EBI,
  author =       "A. Kalaivani and K. Swetha",
  title =        "An Enhanced Bidirectional Insertion Sort Over
                 Classical Insertion Sort",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500248",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500248",
  abstract =     "Sorting is a technique which is used to arrange the
                 data in specific order. A sorting technique is applied
                 to rearrange the elements in numerical order as
                 ascending order or descending order or for words in
                 alphabetical order. In this paper, we propose an
                 efficient sorting algorithm known as Enhanced
                 Bidirectional Insertion Sorting algorithm which is
                 developed from insertion sort concept. A comparative
                 analysis is done for the proposed Enhanced
                 Bidirectional Insertion Sort algorithm with the
                 selection sort and insertion sort algorithms. When
                 compared to insertion sort algorithm the proposed
                 algorithm outperforms with less number of comparisons
                 in worst case and average case computing time. The
                 proposed algorithm works efficiently for duplicated
                 elements which is the advanced improvement and the
                 results are proved.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shaikh:2021:STB,
  author =       "Ayesha S. Shaikh and Vibha D. Patel",
  title =        "Significance of the Transition to Biometric Template
                 Protection: Explore the Future",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S021946782150025X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed May 5 11:23:13 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782150025X",
  abstract =     "The IT security paradigm evolves from secret-based to
                 biometric identity-based. Biometric identification has
                 gradually become more popular in recent years for
                 handheld devices. Privacy-preserving is a key concern
                 when biometrics is used in authentication systems in
                 the present world today. Nowadays, the declaration of
                 biometric traits has been imposed not only by the
                 government but also by many private entities. There are
                 no proper mechanisms and assurance that biometric
                 traits will be kept safe by such entities. The
                 encryption of biometric traits to avoid privacy attacks
                 is a giant problem. Hence, state-of-the-art safety and
                 security technological solutions must be devised to
                 prevent the loss and misuse of such biometric traits.
                 In this paper, we have identified different cancelable
                 biometrics methods with the possible attacks on the
                 biometric traits and directions on possible
                 countermeasures in order to design a secure and
                 privacy-preserving biometric authentication system. We
                 also proposed a highly secure method for cancelable
                 biometrics using a non-invertible function based on
                 Discrete Cosine Transformation and Index of max
                 hashing. We tested and evaluated the proposed novel
                 method on a standard dataset and achieved good
                 results.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kiran:2021:CSI,
  author =       "S. Shashi Kiran and K. V. Suresh",
  title =        "Challenges in Sparse Image Reconstruction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467821500261",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500261",
  abstract =     "Handling huge amount of data from different sources
                 more so in the images is the latest challenge. One of
                 the solutions to this is sparse representation. The
                 idea of sparsity has been receiving much attention
                 recently from many researchers in the areas such as
                 satellite image processing, signal processing, medical
                 image processing, microscopy image processing, pattern
                 recognition, neuroscience, seismic imaging, etc. Many
                 algorithms have been developed for various areas of
                 sparse representation. The main objective of this paper
                 is to provide a comprehensive study and highlight the
                 challenges in the area of sparse representation which
                 will be helpful for researchers. Also, the current
                 challenges and opportunities of applying sparsity to
                 image reconstruction, namely, image super-resolution,
                 image denoising and image restoration are discussed.
                 This survey on sparse representation categorizes the
                 existing methods into three groups: dictionary learning
                 approach, greedy strategy approximation approach and
                 deep learning approach.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sowmyayani:2021:MTC,
  author =       "S. Sowmyayani and V. Murugan",
  title =        "Multi-Type Classification Comparison of Mammogram
                 Abnormalities",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500273",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500273",
  abstract =     "Cancer is a life-threatening disease which reduces the
                 lifespan of humans. If the disease is treated early,
                 the lifespan can be extended. This paper provides a
                 useful method for detecting the abnormalities in the
                 mammograms. The proposed method uses four phases such
                 as pre-processing, segmentation, feature extraction and
                 classification. In the pre-processing phase, median
                 filter is utilized to enhance the quality of an image.
                 The pre-processed image is then segmented by fuzzy C
                 means (FCM). Three different features such as
                 Gaussian--Hermite moments (GHM), Jacobi moments and
                 pseudo Zernike moments (PZM) are extracted from the
                 segmented image. Finally, extreme learning machine
                 (ELM) classifier identifies the normal, malignant and
                 benign kinds of cancer. This method is compared with
                 four different classifiers. The proposed method is
                 tested on mammographic image analysis society (MIAS)
                 dataset and the performance is evaluated against
                 several analogous approaches in terms of accuracy,
                 sensitivity and specificity. The proposed approach
                 substantially provides the best result.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mondal:2021:SGS,
  author =       "Md. Abdul Mannan Mondal and Mohammad Haider Ali",
  title =        "Self-guided Stereo Correspondence Estimation
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500285",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500285",
  abstract =     "This paper introduces an innovative algorithm,
                 ``Self-guided Stereo Correspondence'' (SGSC), that is
                 directed by photometric properties of the candidate
                 pixels. As the photometric properties of reference
                 image (left image) pixel and its neighbor's pixel are
                 similar in most cases, so the upcoming corresponding
                 pixel exists in the surrounding of the previous
                 matching pixel. Searching performance is greatly
                 improved by utilizing this photometric property of the
                 candidate pixels. The searching performance is further
                 improved by applying the pioneering threshold
                 technique. These two key techniques sufficiently
                 reduced the computational cost with no degradation of
                 accuracy. The achievements of the proposed method are
                 testified on Middlebury standard Stereo Datasets of
                 2003 and 2006 and the Middlebury latest Optical Flow
                 Dataset. Finally, the proposed method is compared with
                 present state-of-the-art methods and our SGSC outdoes
                 the latest methods in terms of speed, visualization of
                 hidden ground truth, 3D reconstruction and accuracy.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Basheera:2021:GMS,
  author =       "Shaik Basheera and M. Satya Sai Ram",
  title =        "{Gray} Matter Segmentation of Brain {MRI} Using Hybrid
                 Enhanced Independent Component Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500297",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500297",
  abstract =     "One of the primary pre-processing tasks of medical
                 image analysis is segmentation; it is used to diagnose
                 the abnormalities in the tissues. As the brain is a
                 complex organ, anatomical segmentation of brain tissues
                 is a challenging task. Segmented gray matter is
                 analyzed for early diagnosis of neurodegenerative
                 disorders. In this endeavor, we used enhanced
                 independent component analysis to perform segmentation
                 of gray matter in noise-free and noisy environments. We
                 used modified k -means, expectation--maximization and
                 hidden Markov random field to provide better spatial
                 relation to overcome inhomogeneity, noise and low
                 contrast. Our objective is achieved using the following
                 two steps: (i) Irrelevant tissues are stripped from the
                 MRI using skull stripping algorithm. In this algorithm,
                 sequence of threshold, morphological operations and
                 active contour are applied to strip the unwanted
                 tissues. (ii) Enhanced independent component analysis
                 is used to perform segmentation of gray matter. The
                 proposed approach is applied on both T1w MRI and T2w
                 MRI images at different noise environments such as salt
                 and pepper noise, speckle noise and Rician noise. We
                 evaluated the performance of the approach using Jaccard
                 index, Dice coefficient and accuracy. The parameters
                 are further compared with existing frameworks. This
                 approach gives better segmentation of gray matter for
                 the diagnosis of atrophy changes in brain MRI.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rawal:2021:DMW,
  author =       "Kirti Rawal and Gaurav Sethi",
  title =        "Design of Matched Wavelet Using Improved Genetic
                 Algorithm for Heart Rate Variability Analysis of the
                 Menstrual Cycle",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500303",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500303",
  abstract =     "The matched wavelet is designed in this paper using an
                 improved genetic algorithm for detecting the Heart Rate
                 Variability (HRV) variations within phases of the
                 menstrual cycle accurately. The idea of an improved
                 genetic algorithm is to use an optimization technique
                 like least mean square (LMS) before the genetic
                 algorithm. The advantage of using the LMS prior to the
                 genetic algorithm is to optimize the data before giving
                 to the genetic algorithm, thereby limiting the area of
                 the search for an optimal solution. The results show
                 that matched wavelets created using an improved genetic
                 algorithm can detect the HRV variations accurately in
                 the standing and laying postures.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wagdy:2021:DDI,
  author =       "Marian Wagdy and Khaild Amin and Mina Ibrahim",
  title =        "Dewarping Document Image Techniques: Survey and
                 Comparative Study",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500315",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500315",
  abstract =     "In recent years, everyone has his/her own handheld
                 digital devices such as PDAs and camera phones which
                 are used to capture any documents, for example,
                 posters, magazine and books. This is the simplest way
                 to disseminating and collecting information.
                 Unfortunately, the snapshot of this document in an
                 uncontrolled environment has been suffering from
                 different perspectives and geometric distortions,
                 especially when a picture is taken from rolled
                 document, page of thick book, multi-folded documents
                 and crumpled pages. In such cases, the most common
                 distortion appeared is warping text lines. In this
                 paper, we present a survey and a comparative study of
                 document image dewarping techniques which aim to solve
                 the curled lines and geometric distortion problems. We
                 introduce a new classification of the available
                 dewarping document image techniques and investigate
                 their available datasets. Finally, we present the
                 evaluation metric to test these techniques.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2021:SIH,
  author =       "Uche A. Nnolim",
  title =        "Single Image De-Hazing via Multiscale Wavelet
                 Decomposition and Estimation with Fractional
                 Gradient-Anisotropic Diffusion Fusion",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500327",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500327",
  abstract =     "This paper presents algorithms based on fractional
                 multiscale gradient fusion and multilevel wavelet
                 decomposition for underwater and hazy image
                 enhancement. The algorithms utilize partial
                 differential equation (PDE)-generated low- and
                 high-frequency images fused via gradient domain and
                 anisotropic diffusion. Furthermore, wavelet multi-level
                 decomposition, estimation and adjustment of detail and
                 approximation coefficients are employed in improving
                 local and global enhancement. Solutions to halo effect
                 are also developed using compressive bilateral filters
                 or other nonlinear/nonlocal means filter. Ultimately,
                 experimental comparisons indicate that the proposed
                 methods surpass or are comparable to several algorithms
                 from the literature.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arora:2021:CHM,
  author =       "Tanvi Arora",
  title =        "Classification of Human Metaspread Images Using
                 Convolutional Neural Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500339",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500339",
  abstract =     "Chromosomes are the genetic information carriers. Any
                 modification to the structure or the number of
                 chromosomes results in a medical condition termed as
                 genetic defect. In order to uncover the genetic
                 defects, the chromosomes are imaged during the cell
                 division process. The images thus generated are termed
                 as metaspread images and are used for identifying the
                 genetic defects. It has been observed that the
                 metaspread images generally suffer from intensity
                 inhomogeneity and the chromosomes are also present in
                 varied orientations, and as a result finding genetic
                 defects from such images is a tedious process.
                 Therefore, cytogeneticists manually select the images
                 that can be used for the purpose of uncovering the
                 genetic defects and the generation of the karyotype. In
                 the proposed approach, a novel method is being
                 presented using DenseNet architecture of the
                 convolutional neural networks-based classifier, which
                 classifies the human metaspread images into two
                 distinct categories, namely, analyzable and
                 non-analyzable based on the orientation of the
                 chromosomes present in the metaspread images. This
                 classification process will help to select the most
                 prominent metaspread images for karyotype generation
                 that has least amount of touching and overlapping
                 chromosomes. The proposed method is novel in comparison
                 to the earlier methods as it works on any type of
                 image, be it G band images, MFISH images or the
                 Q-banded images. The proposed method has been trained
                 by using a ground truth of 156{\nobreakspace}40750
                 metaspread images. The proposed classifier has been
                 able to achieve an error rate of 1.46\%.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gudise:2021:MBI,
  author =       "Sandhya Gudise and Giri Babu Kande and T. Satya
                 Savithri",
  title =        "{MR} Brain Image Segmentation to Detect White Matter,
                 Gray Matter, and Cerebro Spinal Fluid Using {TLBO}
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500340",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500340",
  abstract =     "This paper proposes an advanced and precise technique
                 for the segmentation of Magnetic Resonance Image (MRI)
                 of the brain. Brain MRI segmentation is to be familiar
                 with the anatomical structure, to recognize the
                 deformities, and to distinguish different tissues which
                 help in treatment planning and diagnosis. Nature's
                 inspired population-based evolutionary algorithms are
                 extremely popular for a wide range of applications due
                 to their best solutions. Teaching Learning Based
                 Optimization (TLBO) is an advanced population-based
                 evolutionary algorithm designed based on Teaching and
                 Learning process of a classroom. TLBO uses common
                 controlling parameters and it won't require
                 algorithm-specific parameters. TLBO is more appropriate
                 to optimize the real variables which are fuzzy valued,
                 computationally efficient, and does not require
                 parameter tuning. In this work, the pixels of the brain
                 image are automatically grouped into three distinct
                 homogeneous tissues such as White Matter (WM), Gray
                 Matter (GM), and Cerebro Spinal Fluid (CSF) using the
                 TLBO algorithm. The methodology includes skull
                 stripping and filtering in the pre-processing stage.
                 The outcomes for 10 MR brain images acquired by
                 utilizing the proposed strategy proved that the three
                 brain tissues are segmented accurately. The
                 segmentation outputs are compared with the ground truth
                 images and high values are obtained for the measure's
                 sensitivity, specificity, and segmentation accuracy.
                 Four different approaches, namely Particle Swarm
                 Optimization (PSO), Genetic Algorithm (GA), Bacterial
                 Foraging Algorithm (BFA), and Electromagnetic
                 Optimization (EMO) are likewise implemented to compare
                 with the results of the proposed methodology. From the
                 results, it can be proved that the proposed method
                 performed effectively than the other.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shivsharan:2021:DRD,
  author =       "Nitin Shivsharan and Sanjay Ganorkar",
  title =        "Diabetic Retinopathy Detection Using Optimization
                 Assisted Deep Learning Model: Outlook on Improved Grey
                 Wolf Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500352",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500352",
  abstract =     "In recent days, study on retinal image remains a
                 significant area for analysis. Several retinal diseases
                 are identified by examining the differences occurring
                 in the retina. Anyhow, the major shortcoming between
                 these analyses was that the identification accuracy is
                 not satisfactory. The adopted framework includes two
                 phases namely; (i) feature extraction and (ii)
                 classification. Initially, the input fundus image is
                 subjected to the feature extraction process, where the
                 features like Local Binary Pattern (LBP), Local Vector
                 Pattern (LVP) and Local Tetra Patterns (LTrP) are
                 extracted. These extracted features are subjected to
                 the classification process, where the Deep Belief
                 Network (DBN) is used as the classifier. In addition,
                 to improve the accuracy, the activation function and
                 hidden neurons of DBN are optimally tuned by means of
                 the Self Improved Grey Wolf Optimization (SI-GWO).
                 Finally, the performance of implemented work is
                 compared and proved over the conventional models.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Salehi:2021:NHF,
  author =       "Hadi Salehi and Javad Vahidi",
  title =        "A Novel Hybrid Filter for Image Despeckling Based On
                 Improved Adaptive {Wiener} Filter, Bilateral Filter and
                 Wavelet Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500364",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500364",
  abstract =     "Images are widely used in engineering. But, some
                 images such as medical ultrasound images are mainly
                 degraded by an intrinsic noise called speckle.
                 Therefore, de-speckling is a main pre-processing stage
                 for degraded images. In this paper, we suggest three
                 phases and three denoising filters. In the first phase,
                 the coefficient of variation is computed from the noisy
                 image. Next, fuzzy c-means (FCM) is applied to the
                 coefficients of variation. Applying FCM leads to the
                 fuzzy classification of image regions. Next, the second
                 phase is a hybrid of the three denoising filters. Fast
                 bilateral filter (BF) for homogeneous regions, improved
                 the adaptive wiener filters (AWFs) and wavelet filter
                 that are applied on homogeneous, detail and edge
                 regions, respectively. The proposed improved AWF has
                 been developed from the AWF. In the third phase, the
                 output image is evaluated by the fuzzy logic approach.
                 Thus, with three phases, the proposed method has a
                 better image detail preservation compared to some other
                 standard methods. The experimental outcomes show that
                 the proposed denoising algorithm is able to preserve
                 image details and edges compared with other
                 de-speckling methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rema:2021:EHC,
  author =       "N. R. Rema and P. Mythili",
  title =        "Extremely High Compression and Identification of
                 Fingerprint Images Using {SA4} Multiwavelet Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500376",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500376",
  abstract =     "The aim of any fingerprint image compression technique
                 is to achieve a maximum amount of compression with an
                 adequate quality compressed image which is suitable for
                 fingerprint recognition. Currently available techniques
                 in the literature provide 100\% recognition only up to
                 a compression ratio of 180:1. The performance of any
                 identification technique inherently depends on the
                 techniques with which images are compressed. To improve
                 the identification accuracy while the images are highly
                 compressed, a multiwavelet-based identification
                 approach is proposed in this paper. Both decimated and
                 undecimated coefficients of SA4 (Symmetric
                 Antisymmetric) multiwavelet are used as features for
                 identification. A study is conducted on the
                 identification performance of the multiwavelet
                 transform with various sizes of images compressed using
                 both wavelets and multiwavelets for fair comparison. It
                 was noted that for images with size power of 2, the
                 decimated multiwavelet-based compression and
                 identification give a better performance compared to
                 other combinations of compression/identification
                 techniques whereas for images with size not a power of
                 2, the undecimated multiwavelet transform gives a
                 better performance compared to other techniques. A
                 100\% identification accuracy was achieved for the
                 images from NIST-4, NITGEN, FVC2002DB3\_B,
                 FVC2004DB2\_B and FVC2004DB1\_B databases for
                 compression ratios up to 520:1, 210:1, 445:1, 545:1 and
                 1995:1, respectively.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zarif:2021:VIC,
  author =       "Sameh Zarif and Mina Ibrahim",
  title =        "Video Inpainting: A Complete Framework",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500388",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500388",
  abstract =     "Reconstructing and repairing of corrupted or missing
                 parts after object removal in digital video is an
                 important trend in artwork restoration. Video
                 inpainting is an active subject in video processing,
                 which deals with the recovery of the corrupted or
                 missing data. Most previous video inpainting approaches
                 consume more time in extensive search to find the best
                 patch to restore the damaged frames. In addition to
                 that, most of them cannot handle the gradual and sudden
                 illumination changes, dynamic background, full object
                 occlusion, and object changes in scale. In this paper,
                 we present a complete video inpainting framework
                 without the extensive search process. The proposed
                 framework consists of a segmentation stage based on
                 low-resolution version and background subtraction. A
                 background inpainting stage is applied to restore the
                 damaged background regions after static or moving
                 object removal based on the gray-level co-occurrence
                 matrix (GLCM). A foreground inpainting stage is based
                 on objects repository. GLCM is used to complete the
                 moving occluded objects during the occlusion. The
                 proposed method reduces the inpainting time from hours
                 to a few seconds and maintains the spatial and temporal
                 consistency. It works well when the background has
                 clutter or fake motion, and it can handle the changes
                 in object size and in posture. Moreover, it is able to
                 handle full occlusion and illumination changes.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Siri:2021:ALB,
  author =       "Sangeeta K. Siri and S. Pramod Kumar and Mrityunjaya
                 V. Latte",
  title =        "Accurate Liver Border Identification Model in {CT}
                 Scan Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S021946782150039X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782150039X",
  abstract =     "The liver is an important organ in human body with
                 certain variations in its edges, color, shape and pixel
                 intensity distribution. These uncertainties may be
                 because of various liver pathologies, hereditary or
                 both. Along with it, liver has close proximity to its
                 nearby organs. Hence, identifying liver in scanned
                 images is a challenging step in image processing. This
                 task becomes more imprecise when liver diseases are
                 present at the edges. The liver segmentation is
                 prerequisite for liver volumetry, computer-based
                 surgery planning, liver surgery modelling, surgery
                 training, 3D view generation, etc. The proposed hybrid
                 segmentation method overcomes the problems and
                 identifies liver boundary in Computed-Tomography (CT)
                 scan images accurately. In this paper, the first step
                 is to study statistics of pixel intensity distribution
                 within liver image, and novel methodology is designed
                 to obtain thresholds. Then, threshold-based
                 segmentation is applied which separates the liver from
                 abdominal CT scan images. In the second step, liver
                 edge is corrected using improved chain code and
                 Bresenham pixel interconnection methods. This provides
                 a precise liver image. The initial points are located
                 inside the liver region without user interventions.
                 These initial points evolve outwardly using Fast
                 Marching Method (FMM), identifying the liver boundary
                 accurately in CT abdominal scan images.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bentahar:2021:HTB,
  author =       "Tarek Bentahar and Atef Bentahar and Riad Saidi and
                 Hichem Mayache and Karim Ferroudji",
  title =        "Hybrid Technique of the Branch-Cut and the
                 Quality-Guided for {inSAR} Phase Unwrapping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "03",
  pages =        "??--??",
  month =        jul,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500406",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Jul 5 15:21:12 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500406",
  abstract =     "Phase unwrapping is a key step for interferometric
                 synthetic aperture radar imaging. It is widely used for
                 earth mapping and surface change detection. Several
                 residue-immune phase unwrapping algorithms have been
                 proposed; among them, we find branch-cut and
                 quality-guided in the path-following category.
                 Branch-cut methods are usually faster than the
                 quality-guided techniques; however, the accuracy of
                 their unwrapped phase images is lower. In this paper, a
                 hybrid model which combines both algorithms is proposed
                 in order to establish a satisfactory compromise between
                 processing time and accuracy. In order to verify the
                 usefulness of the proposed hybridization, it is tested
                 on simulated and real inSAR data. The obtained results
                 are compared with the two methods under several
                 relevant metrics.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Brown:2021:CSF,
  author =       "Kyle Brown and Nikolaos Bourbakis",
  title =        "Curve and Surface Fitting Techniques in Computer
                 Vision",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467821500418",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500418",
  abstract =     "Curve and surface-fitting are classic problems of
                 approximation that find use in many fields, including
                 computer vision. There are two broad approaches to the
                 problem --- interpolation, which seeks to fit points
                 exactly, and regression, which seeks a rougher
                 approximation which is more robust to noise. This
                 survey looks at several techniques of both kinds, with
                 a particular focus on applications in computer vision.
                 We make use of an empirical first-level evaluation
                 approach which scores the techniques on multiple
                 features based on how important they are to users of
                 the technique and developers. This provides a quick
                 summary of the broad applicability of the technique to
                 most situations, rather than a deep evaluation of the
                 performance and accuracy of the technique obtained by
                 running it on several datasets.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pervej:2021:RTC,
  author =       "Masud Pervej and Sabuj Das and Md. Parvez Hossain and
                 Md. Atikuzzaman and Md. Mahin and Muhammad Aminur
                 Rahaman",
  title =        "Real-Time Computer Vision-Based {Bangla} Vehicle
                 License Plate Recognition using Contour Analysis and
                 Prediction Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S021946782150042X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782150042X",
  abstract =     "Computer vision-based recognition of Bangle vehicle
                 license plates (LPs) is an arduous task in dirty and
                 muddy situations. This paper proposes an efficient
                 method for real-time computer vision-based recognition
                 of Bangla vehicle LPs using contour analysis and
                 prediction algorithms. The method initially applies
                 gray scaling the input RGB images, histogram
                 equalization to improve the grayscale image quality,
                 edge detection using Sobel edge detector, and adaptive
                 thresholding to convert it to a binary image. The
                 system localizes the vehicle LP based on the maximum
                 rectangular contour area and converts it into a
                 predefined size. Noise removal technique using
                 morphological dilation and erosion operation is used,
                 followed by Gaussian filtering on binary image to
                 improve the image quality further. The system clusters
                 the two-lined LP into seven clusters. The
                 sub-clustering is applied on specific clusters and
                 makes 68 individual sub-clusters. The system extracts
                 vector contour (VC) from each 68 individual classes.
                 After VC extraction, the system normalizes it into a q
                 predefined length. The system applies inter co-relation
                 function (ICF) to categorize each sub-cluster to its
                 previously defined individual class. For that, it
                 calculates the maximum similarity between test and
                 previously trained VCs. The system applies the
                 dependency prediction algorithm in parallel to predict
                 the whole string (district name) in the cluster-1 based
                 on previously categorized class or classes (starting
                 character or characters of the district part). (Metro)
                 or (null) from cluster-2, ``-'' (hyphen) from cluster-3
                 and 6 are predicted automatically as their positions
                 are fixed. The system is trained using 68 classes in
                 which each class contains 100 samples generated by the
                 augmentation technique. The system is tested using
                 another set of 68 classes with a total of
                 68{\texttimes}100=6800 images acquiring the recognition
                 accuracy of 96.62\% with the mean computational cost of
                 8.363 ms/f. The system is also tested using 500 vehicle
                 whole Bangla LPs achieving the mean whole LP
                 recognition accuracy of 95.41\% with a mean
                 computational cost of 35.803 ms/f.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Patel:2021:ELB,
  author =       "Krina Patel and Dippal Israni and Dweepna Garg",
  title =        "An Efficient Local Block {Sobolev} Gradient and
                 {Laplacian} Approach for Elimination of Atmospheric
                 Turbulence",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500431",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500431",
  abstract =     "A long range observing systems can be sturdily
                 affected by scintillations. These scintillations are
                 caused by changes in atmospheric conditions. In recent
                 years, various turbulence mitigation approaches for
                 turbulence mitigation have been exhibiting a promising
                 nature. In this paper, we propose an effectual method
                 to alleviate the effects of atmospheric distortion on
                 observed images and video sequences. These sequences
                 are mainly affected through floating air turbulence
                 which can severely degrade the image quality. The
                 existing algorithms primarily focus on the removal of
                 turbulence and provides a solution only for static
                 scenes, where there is no moving entity (real motion).
                 As in the traditional SGL algorithm, the updated frame
                 is iteratively used to correct the turbulence. This
                 approach reduces the turbulence effect. However, it
                 imposes some artifacts on the real motion that blurs
                 the object. The proposed method is an alteration of the
                 existing Sobolev Gradient and Laplacian (SGL) algorithm
                 to eliminate turbulence. It eliminates the ghost
                 artifact formed on moving object in the existing
                 approach. The proposed method alleviates turbulence
                 without harming the moving objects in the scene. The
                 method is demonstrated on significantly distorted
                 sequences provided by OTIS and compared with the SGL
                 technique. The information conveyed in the scene
                 becomes clearly visible through the method on exclusion
                 of turbulence. The proposed approach is evaluated using
                 standard performance measures such as MSE, PSNR and
                 SSIM. The evaluation results depict that the proposed
                 method outperforms the existing state-of-the-art
                 approaches for all three standard performance
                 measures.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nivedita:2021:ICV,
  author =       "M. Nivedita and Priyanka Chandrashekar and Shibani
                 Mahapatra and Y. Asnath Victy Phamila and Sathish Kumar
                 Selvaperumal",
  title =        "Image Captioning for Video Surveillance System using
                 Neural Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500443",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500443",
  abstract =     "Security has always been of paramount importance to
                 humans. In the absence of a sense of security at one's
                 workplace, home or anywhere else, people feel uneasy
                 and vulnerable. With the improvement of modern
                 technology, along with the lack of time at hand, the
                 need for faster, efficient, accurate as well as
                 low-cost security techniques is more than ever. Image
                 Captioning for Video Surveillance System is proposed to
                 develop visual systems that generate contextual
                 descriptions about objects in images, and then use
                 these descriptions to provide information of the scene
                 that needs to be secured. The proposed system uses a
                 neural network model composed of a Convolutional Neural
                 Network (CNN) and Long Short-Term Memory (LSTM) to
                 caption the incoming video feed. The main significance
                 of this paper is in integrating the system with
                 Discrete Wavelet Transform (DWT), which is applied on
                 the incoming video feed, so that the compressed LL band
                 frames transferred wirelessly to the model are smaller
                 in comparison, leading to less transfer time and faster
                 processing by the model.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pal:2021:RMD,
  author =       "Tannistha Pal",
  title =        "A Robust Method for Dehazing of Single Image with Sky
                 Region Detection and Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500455",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500455",
  abstract =     "In recent times, there has been a tremendous progress
                 in image dehazing for computer vision applications,
                 while the sky region processed by these algorithms
                 tends to degrade by noise and color distortion. In this
                 paper, an improved dark channel prior algorithm is
                 proposed which detects the sky region first and divides
                 the image into sky region and non-sky region and then
                 estimates the transmission of two parts separately,
                 followed by combining with refining step. The proposed
                 algorithm also accurately corrects the transmission of
                 the sky region to avoid noise and color distortion.
                 Experimental results show a greater quality improvement
                 in the output images than the existing strategies.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2021:ECB,
  author =       "Gangavarapu Venkata Satya Kumar and Pillutla Gopala
                 Krishna Mohan",
  title =        "Enhanced Content-Based Image Retrieval Using
                 Information Oriented Angle-Based Local Tri-Directional
                 {Weber} Patterns",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500467",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500467",
  abstract =     "In diverse computer applications, the analysis of
                 image content plays a key role. This image content
                 might be either textual (like text appearing in the
                 images) or visual (like shape, color, texture). These
                 two image contents consist of image's basic features
                 and therefore turn out to be as the major advantage for
                 any of the implementation. Many of the art models are
                 based on the visual search or annotated text for
                 Content-Based Image Retrieval (CBIR) models. There is
                 more demand toward multitasking, a new method needs to
                 be introduced with the combination of both textual and
                 visual features. This paper plans to develop the
                 intelligent CBIR system for the collection of different
                 benchmark texture datasets. Here, a new descriptor
                 named Information Oriented Angle-based Local
                 Tri-directional Weber Patterns (IOA-LTriWPs) is
                 adopted. The pattern is operated not only based on
                 tri-direction and eight neighborhood pixels but also
                 based on four angles 0\textdegree, 45\textdegree,
                 90\textdegree, and 135\textdegree. Once the patterns
                 concerning tri-direction, eight neighborhood pixels,
                 and four angles are taken, the best patterns are
                 selected based on maximum mutual information. Moreover,
                 the histogram computation of the patterns provides the
                 final feature vector, from which the new weighted
                 feature extraction is performed. As a new contribution,
                 the novel weight function is optimized by the Improved
                 MVO on random basis (IMVO-RB), in such a way that the
                 precision and recall of the retrieved image is high.
                 Further, the proposed model has used the logarithmic
                 similarity called Mean Square Logarithmic Error (MSLE)
                 between the features of the query image and trained
                 images for retrieving the concerned images. The
                 analyses on diverse texture image datasets have
                 validated the accuracy and efficiency of the developed
                 pattern over existing.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kang:2021:GIB,
  author =       "Henry Kang and Ioannis Stamoulis",
  title =        "{Gaussian} Image Binarization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500479",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500479",
  abstract =     "Line drawing and screentoning are two distinct areas
                 of study in non-photorealistic rendering, where the
                 former emphasizes object contours, while the latter
                 conveys tone and shading information on object
                 surfaces. As these two problems are concerned with
                 different yet equally important features, either method
                 seldom delivers a complete description of the scene
                 when used alone. Yet, research community has largely
                 treated them as separate problems and thus resulted in
                 two entirely different sets of solutions, complicating
                 both implementation and usage. In this paper, we
                 present a stylistic image binarization method called
                 {\em hybrid difference of Gaussians (HDoG)\/} that
                 performs both line drawing and screentoning in a
                 unified framework. Our method is based upon two
                 different extensions of DoG operator: one for line
                 extraction, and the other for tone description. In
                 particular, we propose an extension called {\em
                 adaptive DoG}, that uses luminance as weight to
                 automatically generate screentone that adapts to the
                 local tone. Experimental results demonstrate that our
                 hybrid method effectively generates aesthetically
                 pleasing image binarizations that encompass both line
                 drawing and screentoning, closely resembling
                 professional pen-and-ink illustrations. Also, being
                 based on Gaussian filtering, our method is very fast
                 and also easy to implement.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Swamy:2021:HIC,
  author =       "A. S. Anand Swamy and N. Shylashree",
  title =        "{HDR} Image Compression by Multi-Scale down Sampling
                 of Intensity Levels",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500480",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500480",
  abstract =     "HDR images are inherently very large in size compared
                 to normal images. Hence, storage and communication
                 overheads of HDR images are expensive to be used in
                 mobile devices. Hence, invariably image compression is
                 adopted for HDR images. In this paper, HDR image
                 compression is achieved by down sampling the intensity
                 levels while maintaining the dynamic range same as that
                 of the original. This aspect retains the edge
                 information of the images almost intact. Spatial
                 down-sampling process is used to reduce the number of
                 intensity samples. Consequently, this operation lowers
                 the bit depth required to store the corresponding index
                 file which in turn results in image compression.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Panigrahi:2021:JBF,
  author =       "Susant Kumar Panigrahi and Supratim Gupta",
  title =        "Joint Bilateral Filter for Signal Recovery from Phase
                 Preserved Curvelet Coefficients for Image Denoising",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500492",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500492",
  abstract =     "Thresholding of Curvelet Coefficients, for image
                 denoising, drains out subtle signal component in noise
                 subspace. In effect, it also produces ringing artifacts
                 near edges. We found that the noise sensitivity of
                 Curvelet phases{\nobreakspace}40 --- in contrast to
                 their magnitude{\nobreakspace}40 --- reduces with
                 higher noise level. Thus, we preserved the phase of the
                 coefficients below threshold at coarser scale and
                 estimated the corresponding magnitude by Joint
                 Bilateral Filtering (JBF) technique. In contrast to the
                 traditional hard thresholding, the coefficients in the
                 finest scale is estimated using Bilateral Filtering
                 (BF). The proposed filtering approach in the finest
                 scale exhibits better connectedness among the edges,
                 while removing the granular artifacts in the denoised
                 image due to hard thresholding. Finally, the use of
                 Guided Image Filter (GIF) on the Curvelet-based
                 reconstructed image (initial denoised image in spatial
                 domain) ensures the preservation of small image
                 information with sharper edges and textures detail in
                 the final denoised image. The lower noise sensitivity
                 of Curvelet phase at higher noise strength accelerates
                 the performance of proposed method over several
                 state-of-the-art techniques and provides comparable
                 outcome at lower noise levels.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ramwala:2021:RNC,
  author =       "Ojas A. Ramwala and Smeet A. Dhakecha and Chirag N.
                 Paunwala and Mita C. Paunwala",
  title =        "Reminiscent Net: Conditional {GAN}-based Old Image
                 De-Creasing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500509",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500509",
  abstract =     "Documents are an essential source of valuable
                 information and knowledge, and photographs are a great
                 way of reminiscing old memories and past events.
                 However, it becomes difficult to preserve the quality
                 of such ancient documents and old photographs for an
                 extremely long time, as these images usually get
                 damaged or creased due to various extrinsic effects.
                 Utilizing image editing software like Photoshop to
                 manually reconstruct such old photographs and documents
                 is a strenuous and an enduring process. This paper
                 attempts to leverage the generative modeling
                 capabilities of Conditional Generative Adversarial
                 Networks by utilizing specialized architectures for the
                 Generator and the Discriminator. The proposed
                 Reminiscent Net has a U-Net-based Generator with
                 numerous feature maps for complete information transfer
                 with the incorporation of location and contextual
                 details, and the absence of dense layers allows
                 utilization of diverse sized images. Implementation of
                 the PatchGAN-based Discriminator that penalizes the
                 image at the scale of patches has been proposed. NADAM
                 optimizer has been implemented to enable faster and
                 better convergence of the loss function. The proposed
                 method produces visually appealing de-creased images,
                 and experiments indicate that the architecture performs
                 better than various novel approaches, both
                 qualitatively and quantitatively.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kuzhali:2021:AID,
  author =       "S. Elavaar Kuzhali and D. S. Suresh",
  title =        "Automated Image Denoising Model: Contribution Towards
                 Optimized Internal and External Basis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500510",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500510",
  abstract =     "For handling digital images for various applications,
                 image denoising is considered as a fundamental
                 pre-processing step. Diverse image denoising algorithms
                 have been introduced in the past few decades. The main
                 intent of this proposal is to develop an effective
                 image denoising model on the basis of internal and
                 external patches. This model adopts Non-local means
                 (NLM) for performing the denoising, which uses
                 redundant information of the image in pixel or spatial
                 domain to reduce the noise. While performing the image
                 denoising using NLM, ``denoising an image patch using
                 the other noisy patches within the noisy image is done
                 for internal denoising and denoising a patch using the
                 external clean natural patches is done for external
                 denoising''. Here, the selection of optimal block from
                 the entire datasets including internal noisy images and
                 external clean natural images is decided by a new
                 hybrid optimization algorithm. The two renowned
                 optimization algorithms Chicken Swarm Optimization
                 (CSO), and Dragon Fly Algorithm (DA) are merged, and
                 the new hybrid algorithm Rooster-based Levy Updated DA
                 (RLU-DA) is adopted. The experimental results in terms
                 of some relevant performance measures show the
                 promising results of the proposed model with remarkable
                 stability and high accuracy.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ehsaeyan:2021:MIT,
  author =       "Ehsan Ehsaeyan and Alireza Zolghadrasli",
  title =        "A Multilevel Image Thresholding Method Using the
                 {Darwinian} Cuckoo Search Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500522",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500522",
  abstract =     "Image segmentation is a prime operation to understand
                 the content of images. Multilevel thresholding is
                 applied in image segmentation because of its speed and
                 accuracy. In this paper, a novel multilevel
                 thresholding algorithm based on Cuckoo search (CS) is
                 introduced. One of the major drawbacks of metaheuristic
                 algorithms is the stagnation phenomenon which leads to
                 a fall into local optimums and premature convergence.
                 To overcome this shortcoming, the idea of Darwinian
                 theory is incorporated with CS algorithm to increase
                 the diversity and quality of the individuals without
                 decreasing the convergence speed of CS algorithm. A
                 policy of encouragement and punishment is considered to
                 lead searching agents in the search space and reduce
                 the computational time. The algorithm is implemented
                 based on dividing the population into specified groups
                 and each group tries to find a better location. Ten
                 test images are selected to verify the ability of our
                 algorithm using the famous energy curve method. Two
                 popular entropies criteria, Otsu and Kapur, are
                 employed to evaluate the capability of the introduced
                 algorithm. Eight different search algorithms are also
                 implemented and compared with our method. Experimental
                 results manifest that DCS is a powerful tool for
                 multilevel thresholding and the obtained results
                 outperform the CS algorithm and other heuristic search
                 methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pan:2021:SID,
  author =       "Yongpeng Pan and Zhenxue Chen and Xianming Li and
                 Weikai He",
  title =        "Single-Image Dehazing via Dark Channel Prior and
                 Adaptive Threshold",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500534",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500534",
  abstract =     "Due to the haze weather, the outdoor image quality is
                 degraded, which reduces the image contrast, thereby
                 reducing the efficiency of computer vision systems such
                 as target recognition. There are two aspects of the
                 traditional algorithm based on the principle of dark
                 channel to be improved. First, the restored images
                 obviously contain color distortion in the sky region.
                 Second, the white regions in the scene easily affect
                 the atmospheric light estimated. To solve the above
                 problems, this paper proposes a single-image dehazing
                 and image segmentation method via dark channel prior
                 (DCP) and adaptive threshold. The sky region of hazing
                 image is relatively bright, so sky region does not meet
                 the DCP. The sky part is separated by the adaptive
                 threshold, then the scenery and the sky area are
                 dehazed, respectively. In order to avoid the
                 interference caused by white objects to the estimation
                 of atmospheric light, we estimate the value of
                 atmospheric light using the separated area of the sky.
                 The algorithm in this paper makes up for the
                 shortcoming that the algorithm based on the DCP cannot
                 effectively process the hazing image with sky region,
                 avoiding the effect of white objects on estimating
                 atmospheric light. Experimental results show the
                 feasibility and effectiveness of the improved
                 algorithm.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chalamalasetty:2021:RPT,
  author =       "Sai Pratheek Chalamalasetty and Srinivasa Rao
                 Giduturi",
  title =        "Research Perception Towards Copy-Move Image Forgery
                 Detection: Challenges and Future Directions",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500546",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500546",
  abstract =     "In digital images, Copy-Move Forgery is a general kind
                 of forgery techniques. The process of replicating one
                 part of the image within the same image is termed as
                 copy-move forgery. An effective and reliable approach
                 needs to be developed for identifying these forgeries
                 for restoring the image trustworthiness. The main
                 intent of this paper is to sort out the diverse
                 copy-move image forgery detection models. This survey
                 makes an effective literature analysis on a set of
                 literal works from the past 10 years. The analysis is
                 focused on categorizing the models based on
                 transformation models, machine learning algorithms, and
                 other advanced techniques. The main contribution and
                 limitations of the works are clearly pointed out. In
                 addition, the types of datasets and the simulation
                 platforms utilized by different copy-move forgery
                 detection (CMFD) models are analyzed. The performance
                 measures evaluated by different contributions have been
                 observed for making a concluding decision. The
                 utilization of optimization algorithms on copy-move
                 image forgery detection has also been studied. Finally,
                 the research gaps and challenges with future direction
                 are discussed, which is helpful for researchers in
                 developing an efficient CMFD that could attain high
                 performance.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hamroun:2021:NCB,
  author =       "Mohamed Hamroun and Karim Tamine and Frederic Claux
                 and Mourad Zribi",
  title =        "A New Content-Based Image Retrieval System Using Deep
                 Visual Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821500558",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821500558",
  abstract =     "Content-based image retrieval (CBIR) is a technique
                 for images retrieval based on their visual features,
                 i.e. induced by their pixels. The images are,
                 classically, described by the image feature vectors.
                 Those vectors reflect the texture, color or a
                 combination of them. The accuracy of the CBIR system is
                 highly influenced by the (i) definition of the image
                 feature vector describing the image, (ii) indexing and
                 (iii) retrieval process. In this paper, we propose a
                 new CBIR system entitled ISE (Image Search Engine). Our
                 ISE system defines the optimum combination of color and
                 texture features as an image feature vector, including
                 the Particle Swarm Optimization (PSO) algorithm and
                 employing an Interactive Genetic Approach (GA) for the
                 indexing process. The performance analysis shows that
                 our suggested PCM (Proposed Combination Method)
                 upgrades the average precision metric from 66.6\% to
                 89.30\% for the ``Food'' category color histogram, from
                 77.7\% to 100\% concerning CCVs (Color Coherence
                 Vectors) for the ``Flower'' category and from 58\% to
                 87.65\% regarding the DCD (Dominant Color Descriptor)
                 for the ``Building'' category using the Corel dataset.
                 Besides, our ISE system showcases an average precision
                 of 98.23\%, which is significantly higher than other
                 CBIR systems presented in related works.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2021:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 21)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "04",
  pages =        "??--??",
  month =        oct,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821990011",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:54 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821990011",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Naveenkumar:2021:STJ,
  author =       "M. Naveenkumar and S. Domnic",
  title =        "Spatio Temporal Joint Distance Maps for Skeleton-Based
                 Action Recognition Using Convolutional Neural
                 Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "05",
  pages =        "??--??",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467821400015",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:56 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400015",
  abstract =     "Skeleton-based action recognition has become popular
                 with the recent developments in sensor technology and
                 fast pose estimation algorithms. The existing research
                 works have attempted to address the action recognition
                 problem by considering either spatial or temporal
                 dynamics of the actions. But, both the features
                 (spatial and temporal) would contribute to solve the
                 problem. In this paper, we address the action
                 recognition problem using 3D skeleton data by
                 introducing eight Joint Distance Maps, referred to as
                 Spatio Temporal Joint Distance Maps (ST-JDMs), to
                 capture spatio temporal variations from skeleton data
                 for action recognition. Among these, four maps are
                 defined in spatial domain and remaining four are in
                 temporal domain. After construction of ST-JDMs from an
                 action sequence, they are encoded into color images.
                 This representation enables us to fine-tune the
                 Convolutional Neural Network (CNN) for action
                 classification. The empirical results on the two
                 datasets, UTD MHAD and NTU RGB+D, show that ST-JDMs
                 outperforms the other state-of-the-art skeleton-based
                 approaches by achieving recognition accuracies 91.63\%
                 and 80.16\%, respectively.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Deep Neural Networks for Medical
                 Image Detection, Segmentation, and Localization",
}

@Article{Sharma:2021:DDS,
  author =       "Moolchand Sharma and Bhanu Jain and Chetan Kargeti and
                 Vinayak Gupta and Deepak Gupta",
  title =        "Detection and Diagnosis of Skin Diseases Using
                 Residual Neural Networks {(RESNET)}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "05",
  pages =        "??--??",
  month =        dec,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821400027",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:56 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400027",
  abstract =     "Skin diseases have become prevalent in the present
                 times. It has been observed in a study that every year
                 the percentage of global population suffering from skin
                 diseases is 1.79\%. These diseases have a potential to
                 become extremely dangerous if they are not treated in
                 the nascent stages. It is extremely important that skin
                 diseases are detected and diagnosed at the starting
                 stages so that serious risks to life are avoided.
                 Often, exhaustive tests are required so as to arrive on
                 a conclusion regarding skin condition, the patient may
                 be affected with. Thus, an expert system is required
                 that has the ability to identify diseases and propose
                 the required diagnosis. Presently, only a few solutions
                 are available for diagnosis of skin diseases using
                 computerized system but this is an era which is under
                 extensive research and can be developed further. As the
                 existing system has certain loopholes, this system
                 attempts to override the present problems by applying a
                 different approach. As a result of comparison of
                 results from numerous research papers, an expert system
                 has been developed by choosing residual neural networks
                 (ResNet) and this system can be used to aid skin
                 specialists in identifying and diagnosing various major
                 diseases of skin like (Eczema, Psoriasis & Lichen
                 Planus, Benign Tumors, Fungal Infections and Viral
                 Infections) in more effective and efficient manner. The
                 causes for identified skin disease can be outlined
                 through this system and treatment can be provided. We
                 have used Python language for implementing the proposed
                 expert system that uses a 50-layer ResNets for training
                 a dataset that has been taken from DERMNET. We achieved
                 an accuracy of 95\% using ResNet for training of the
                 model and prediction of results at an epoch value of
                 10.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Deep Neural Networks for Medical
                 Image Detection, Segmentation, and Localization",
}

@Article{Gupta:2021:DCB,
  author =       "Isha Gupta and Sheifali Gupta and Swati Singh",
  title =        "Different {CNN}-based Architectures for Detection of
                 Invasive Ductal Carcinoma in Breast Using
                 Histopathology Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "05",
  pages =        "??--??",
  month =        dec,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821400039",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:56 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400039",
  abstract =     "In recent years, many improvements have been made in
                 image processing techniques which aid pathologists to
                 identify cancer cells. Nowadays, convolutional neural
                 networks (CNNs), also known as deep learning algorithms
                 have become popular for the applications of image
                 processing and examination in histopathology image
                 (tissue and cell images). This study aims to present
                 the detection of histopathology images associated to
                 detection of invasive ductal carcinoma (IDC) and
                 non-IDC in breast. However, detection of IDC is a
                 challenging task in histopathology image which needs
                 deep examination as cancer comprises of minor entities
                 with a diversity of forms which can be easily mixed up
                 with different objects or facts contained in image.
                 Hence, the proposed study suggests three types of CNN
                 architectures which is called 8-layer CNNs, 9-layer
                 CNNs and 19-layer CNNs, respectively, in the detecting
                 IDC using histopathology images. The purpose of the
                 study is to identify IDC from histopathology images by
                 taking proper layer in deep layer CNNs with the maximum
                 accuracy, highest sensitivity, precision and least
                 classification error. The result shows better
                 performance for deep layer-convolutional neural
                 networks architecture by using 19-layer CNNs.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Deep Neural Networks for Medical
                 Image Detection, Segmentation, and Localization",
}

@Article{Verma:2021:CBD,
  author =       "Parag Verma and Ankur Dumka and Anuj Bhardwaj and
                 Mukesh Chandra Kestwal",
  title =        "Classifying Breast Density in Mammographic Images
                 Using Wavelet-Based and Fine-Tuned Sensory Neural
                 Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "05",
  pages =        "??--??",
  month =        dec,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821400040",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:56 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400040",
  abstract =     "In this modern world of biomedical medicine, the
                 classification of breast density has been considered a
                 very important part of the process of breast diagnosis.
                 Furthering the same research, this research aims to
                 determine the patient's breast density by mammogram
                 image with the help of modern techniques such as
                 computerized devices and machine learning algorithms,
                 which will greatly help the radiologist. To carry out
                 this process, this research paper introduces a
                 Convolutional Neural Network (CNN) model of deep
                 learning that will work as a basis for waveform
                 conversion and fine-tune. This deep learning model will
                 prove effective in automatically classifying a
                 patient's breast density. With the help of this method,
                 the last two layers which are fully connected are
                 removed and joined with two newly formed layers. This
                 would have helped in addressing a pre-trained AlexNet
                 model that further improved the classification process.
                 In this model, the original or preprocessed images are
                 used at level 1 of the input (which is in sharp
                 contrast to the usual methods based on the CNN model),
                 which also makes the model compatible with the use of
                 redundant wavelet coefficients. Because in the field of
                 radiologists it is very important to underline the
                 difference between scattered density and heterogeneous
                 density, so the main objective of this research is
                 focused on this end. As the proposed method has an
                 accuracy of 82.2\%, it shows a better performance. This
                 research paper further compares the effectiveness and
                 performance of the proposed method to traditional
                 fine-tuning CNN models, with satisfactory results. The
                 comparative results of the proposed method suggest that
                 the proposed method is in the field of radiologists
                 representing a helpful tool. This method may be
                 intended to act as a second eye for doctors in the
                 medical field with the intention of classifying the
                 categories of breast density in the patient during
                 breast cancer screening.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Deep Neural Networks for Medical
                 Image Detection, Segmentation, and Localization",
}

@Article{Tunga:2021:UNM,
  author =       "P. Prakash Tunga and Vipula Singh and V. Sri Aditya
                 and N. Subramanya",
  title =        "{U-Net} Model-Based Classification and Description of
                 Brain Tumor in {MRI} Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "21",
  number =       "05",
  pages =        "??--??",
  month =        dec,
  year =         "2021",
  DOI =          "https://doi.org/10.1142/S0219467821400052",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Mon Dec 27 07:10:56 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400052",
  abstract =     "In this paper, we discuss the classification of the
                 brain tumor in Magnetic Resonance Imaging (MRI) images
                 using the U-Net model, then evaluate parameters that
                 indicate the performance of the model. We also discuss
                 the extraction of the tumor region from brain image and
                 description of the tumor regarding its position and
                 size. Here, we consider the case of Gliomas, one of the
                 types of brain tumors, which occur in common and can be
                 fatal depending on their position and growth. U-Net is
                 a model of Convolutional Neural Network (CNN) which has
                 U-shaped architecture. MRI employs a non-invasive
                 technique and can very well provide soft-tissue
                 contrast and hence, for the detection and description
                 of the brain tumor, this imaging method can be
                 beneficial. Manual delineation of tumors from brain MRI
                 is laborious, time-consuming and can vary from expert
                 to expert. Our work forms a computer aided technique
                 which is relatively faster and reproducible, and the
                 accuracy is very much on par with ground truth. The
                 results of the work can be used for treatment planning
                 and further processing related to storage or
                 transmission of images.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Deep Neural Networks for Medical
                 Image Detection, Segmentation, and Localization",
}

@Article{Shrivastava:2022:BTD,
  author =       "Neeraj Shrivastava and Jyoti Bharti",
  title =        "Breast Tumor Detection in {MR} Images Based on
                 Density",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467822500012",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500012",
  abstract =     "Breast cancer is dangerous in women. It is generally
                 found after the symptoms appear. Detecting the breast
                 cancer at an early stage and understanding the
                 treatment are the most important strategies to prevent
                 death from cancer. Generally, for detection of breast
                 cancer, breast Magnetic Resonance Image (MRI) takes
                 place. It is one of the best approaches to detect tumor
                 in women. In this research paper, a combination of
                 selection methods for seed region growing image
                 segmentation is suggested to detect breast tumor. The
                 suggested method has been divided into following parts:
                 First, the pre-processing of breast image is performed.
                 Second, the automatic threshold for binarization
                 process is calculated. Third, the number of seed points
                 and its position in the breast image are determined
                 automatically using density of pixels value. Fourth, a
                 method for calculation of threshold value is proposed
                 for the purpose of region creation in seed region
                 growing. For the evaluation purpose, the proposed
                 method was applied and tested on the RIDER MRI breast
                 dataset from National Biomedical Imaging Archive
                 (NBIA). After the test was performed, it was observed
                 that proposed algorithm gives 90\% accuracy, 88\% True
                 Negative Fraction, 91\% True Positive Fraction, 10\%
                 Misclassification Rate, 94\% Precision and 86\%
                 Relative Overlap which is better than other existing
                 methods. It not only gives better evaluation measure
                 but also provides segmentation method for multiple
                 tumor detection.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Padhy:2022:CLR,
  author =       "Rajalaxmi Padhy and Shashwat Sourav Swain and Sanjit
                 Kumar Dash and Jibitesh Mishra",
  title =        "Classification of Low-Resolution Satellite Images
                 Using Fractal Augmented Descriptors",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500024",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500024",
  abstract =     "Satellite imagery consists of highly complex spatial
                 features that make it difficult for traditional image
                 processing techniques to use them for classification
                 tasks. In this paper, we propose a novel method to use
                 these hidden fractal information that naturally exist
                 in these satellite images. We have designed a
                 fractal-based descriptor which generates a scale
                 invariant fractal image for easier fractal-based
                 pattern extraction and uses it as an added feature
                 vector that is combined with the original image and fed
                 into a VGG-16 deep learning architecture which
                 successfully classifies even low-resolution satellite
                 images with an f1-score of 0.78.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Suresha:2022:KDB,
  author =       "M. Suresha and D. S. Raghukumar and S. Kuppa",
  title =        "{Kumaraswamy} Distribution Based Bi-histogram
                 Equalization for Enhancement of Microscopic Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500036",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500036",
  abstract =     "Among all image enhancement techniques, histogram
                 equalization is the most used technique. However,
                 preserving brightness is the main issue, and it creates
                 a weird look by destroying its originality. This paper
                 proposes a new method that has command on the
                 brightness issue of histogram equalization to enhance
                 the quality of microscopic images. The method splits
                 the histogram of each color channel into two
                 sub-histograms based on their mean as the threshold and
                 supplanting their cumulative distribution with
                 Kumaraswamy distribution. The proposed method is tested
                 with color microscopic images of cancer-affected lymph
                 nodes gathered from Biological Image Repository IICBU,
                 and objective and subjective assessments confirm that
                 the proposed approach performs more efficiently
                 compared to other state-of-the-art methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Vandana:2022:ARB,
  author =       "Vandana and Navdeep Kaur",
  title =        "Analytical Review of Biometric Technology Employing
                 Vivid Modalities",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500048",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500048",
  abstract =     "The digitalization has been challenged with the
                 security and privacy aspects in each and every field.
                 In addition to numerous authentication methods,
                 biometrics has been popularized as it relies on one's
                 individual behavioral and physical characters. In this
                 context, numerous unimodal and multimodal biometrics
                 have been proposed and tested in the last decade. In
                 this paper, authors have presented a comprehensive
                 survey of the existing biometric systems while
                 highlighting their respective challenges, advantage and
                 limitations. The paper also discusses the present
                 biometric technology market value, its scope, and
                 practical applications in vivid sectors. The goal of
                 this review is to offer a compact outline of various
                 advances in biometrics technology with potential
                 applications using unimodal and multimodal
                 bioinformatics are discussed that would prove to offer
                 a base for any biometric-based future research.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jameel:2022:GSI,
  author =       "Samer Kais Jameel and Jafar Majidpour",
  title =        "Generating Spectrum Images from Different Types ---
                 Visible, Thermal, and Infrared Based on Autoencoder
                 Architecture {(GVTI-AE)}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S021946782250005X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782250005X",
  abstract =     "Recently, numerous challenging problems have existed
                 for transforming different image types (thermal
                 infrared (TIR), visible spectrum, and near-infrared
                 (NIR)). Other types of cameras may lack the ability and
                 features of certain types of frequently-used cameras
                 that produce different types of images. Based on camera
                 features, different applications might emerge from
                 observing a scenario under specific conditions
                 (darkness, fog, night, day, and artificial light). We
                 need to jump from one field to another to understand
                 the scenario better. This paper proposes a fully
                 automatic model (GVTI-AE) to manipulate the
                 transformation into different types of vibrant,
                 realistic images using the AutoEncoder method, which
                 requires neither pre-nor post-processing or any user
                 input. The experiments carried out using the GVTI-AE
                 model showed that the perceptually realistic results
                 produced in the widely available datasets (Tecnocampus
                 Hand Image Database, Carl dataset, and IRIS
                 Thermal/Visible Face Database).",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{BenSalah:2022:FES,
  author =       "Marwa {Ben Salah} and Ameni Yengui and Mahmoud Neji",
  title =        "Feature Extraction and Selection in Archaeological
                 Images for Automatic Annotation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500061",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500061",
  abstract =     "In this paper, we present two steps in the process of
                 automatic annotation in archeological images. These
                 steps are feature extraction and feature selection. We
                 focus our research on archeological images which are
                 very much studied in our days. It presents the most
                 important steps in the process of automatic annotation
                 in an image. Feature extraction techniques are applied
                 to get the feature that will be used in classifying and
                 recognizing the images. Also, the selection of
                 characteristics reduces the number of unattractive
                 characteristics. However, we reviewed various images of
                 feature extraction techniques to analyze the
                 archaeological images. Each feature represents one or
                 more feature descriptors in the archeological images.
                 We focus on the descriptor shape of the archaeological
                 objects extraction in the images using contour
                 method-based shape recognition of the monuments. So,
                 the feature selection stage serves to acquire the most
                 interesting characteristics to improve the accuracy of
                 the classification. In the feature selection section,
                 we present a comparative study between feature
                 selection techniques. Then we give our proposal of
                 application of methods of selection of the
                 characteristics of the archaeological images. Finally,
                 we calculate the performance of two steps already
                 mentioned: the extraction of characteristics and the
                 selection of characteristics.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gogineni:2022:TSP,
  author =       "Rajesh Gogineni and Dhara J. Sangani",
  title =        "A Two-Stage {PAN}-Sharpening Algorithm Based on Sparse
                 Representation for Spectral Distortion Reduction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500073",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500073",
  abstract =     "Inspite of technological advancement, inherent
                 processing capability of current age sensors limits the
                 desired details in the acquired image for variety of
                 remote sensing applications. Pan-sharpening is a
                 prominent scheme to integrate the essential spatial
                 details inferred from panchromatic (PAN) image and the
                 desired spectral information of multispectral (MS)
                 image. This paper presents an effective two-stage
                 pan-sharpening method to produce high resolution
                 multispectral (HRMS) image. The proposed method is
                 based on the premise that the HRMS image can be
                 formulated as an amalgam of spectral and spatial
                 components. The spectral components are estimated by
                 processing the interpolated MS image with a filter
                 approximated with modulation transfer function (MTF) of
                 the sensor. Sparse representation theory is adapted to
                 construct the spatial components. The high-frequency
                 details extracted from the PAN image and its low
                 resolution variant are utilized to construct dual
                 dictionaries. The dictionaries are jointly learned by
                 an efficient training algorithm to enhance the
                 adaptability. The hypothesis of sparse coefficients
                 invariance over scales is also incorporated to reckon
                 the appropriate spatial information. Further, an
                 iterative filtering mechanism is developed to enhance
                 the quality of fused image. Four distinct datasets
                 generated from QuickBird, IKONOS, Pl{\'e}iades and
                 WorldView-2 sensors are used for experimentation. The
                 comprehensive assessment at reduced-scale and
                 full-scale persuade the effectiveness of proposed
                 method in the retention of spectral information and
                 intensification of the spatial details.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dhar:2022:SRR,
  author =       "Soumi Dhar and Shyamosree Pal",
  title =        "Surface Reconstruction: Roles in the Field of Computer
                 Vision and Computer Graphics",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500085",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500085",
  abstract =     "Surface Reconstruction is the most potent aspect of 3D
                 computer vision. It allows recapturing or imitating of
                 the shape of real objects. It also provides sufficient
                 knowledge regarding the mathematical foundation for
                 rendering applications which are widely used for
                 analyzing medical volume data, modeling, 3D interior
                 designing, architectural designing. In our paper, we
                 have mentioned various algorithms and approaches for
                 surface reconstruction and their applications.
                 Moreover, we have tried to emphasize the necessity of
                 surface reconstruction for solving image analysis
                 related problem.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kushwaha:2022:HAR,
  author =       "Arati Kushwaha and Ashish Khare and Manish Khare",
  title =        "Human Activity Recognition Algorithm in Video
                 Sequences Based on Integration of Magnitude and
                 Orientation Information of Optical Flow",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500097",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500097",
  abstract =     "Human activity recognition from video sequences has
                 emerged recently as pivotal research area due to its
                 importance in a large number of applications such as
                 real-time surveillance monitoring, healthcare, smart
                 homes, security, behavior analysis, and many more.
                 However, lots of challenges also exist such as
                 intra-class variations, object occlusion, varying
                 illumination condition, complex background, camera
                 motion, etc. In this work, we introduce a novel feature
                 descriptor based on the integration of magnitude and
                 orientation information of optical flow and histogram
                 of oriented gradients which gives an efficient and
                 robust feature vector for the recognition of human
                 activities for real-world environment. In the proposed
                 approach first we computed magnitude and orientation of
                 the optical flow separately then a local-oriented
                 histogram of magnitude and orientation of motion flow
                 vectors are computed using histogram of oriented
                 gradients followed by linear combination feature fusion
                 strategy. The resultant features are then processed by
                 a multiclass Support Vector Machine (SVM) classifier
                 for activity recognition. The experimental results are
                 performed over different publically available benchmark
                 video datasets such as UT interaction, CASIA, and
                 HMDB51 datasets. The effectiveness of the proposed
                 approach is evaluated in terms of six different
                 performance parameters such as accuracy, precision,
                 recall, specificity, F -measure, and Matthew's
                 correlation coefficient (MCC). To show the significance
                 of the proposed method, it is compared with the other
                 state-of-the-art methods. The experimental result shows
                 that the proposed method performs well in comparison to
                 other state-of-the-art methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ravikumar:2022:EMB,
  author =       "M. Ravikumar and B. J. Shivaprasad and D. S. Guru",
  title =        "Enhancement of {MRI} Brain Images Using Notch Filter
                 Based on Discrete Wavelet Transform",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500103",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500103",
  abstract =     "In this work, we have proposed Notch filter method to
                 enhance MRI brain images. The proposed method performs
                 better when compared with the existing methods from the
                 literature. The performance is evaluated using
                 quantitative measures like Michelon Contrast (MC),
                 entropy, Peak Signal-to-Noise Ratio (PSNR), Structure
                 Similarity Index Measurement (SSIM) and Absolute Mean
                 Brightness Error (AMBE), as a parameter on publically
                 available BRATS-2018 & 2019 dataset. Overall, the
                 proposed method performs well in comparison to the
                 other existing methods.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhardwaj:2022:IAU,
  author =       "Anuj Bhardwaj and Vivek Singh Verma and Sandesh
                 Gupta",
  title =        "Image Authentication Using Block Truncation Coding in
                 Lifting Wavelet Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500115",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500115",
  abstract =     "Image watermarking is one of the most accepted
                 solutions protecting image authenticity. The method
                 presented in this paper not only provides the desired
                 outcome also efficient in terms of memory requirements
                 and preserving image characteristics. This scheme
                 effectively utilizes the concepts of block truncation
                 coding (BTC) and lifting wavelet transform (LWT). The
                 BTC method is applied to observe the binary watermark
                 image corresponding to its gray-scale image. Whereas,
                 the LWT is incorporated to transform the cover image
                 from spatial coordinates to corresponding transform
                 coordinates. In this, a quantization-based approach for
                 watermark bit embedding is applied. And, the extraction
                 of binary watermark data from the attacked watermarked
                 image is based on adaptive thresholding. To show the
                 effectiveness of the proposed scheme, the experiment
                 over different benchmark images is performed. The
                 experimental results and the comparison with
                 state-of-the-art schemes depict not only the good
                 imperceptibility but also high robustness against
                 various attacks.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ehsaeyan:2022:SDC,
  author =       "Ehsan Ehsaeyan and Alireza Zolghadrasli",
  title =        "A Study on {Darwinian} Crow Search Algorithm for
                 Multilevel Thresholding",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500127",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500127",
  abstract =     "Multilevel thresholding is a basic method in image
                 segmentation. The conventional image multilevel
                 thresholding algorithms are computationally expensive
                 when the number of decomposed segments is high. In this
                 paper, a novel and powerful technique is suggested for
                 Crow Search Algorithm (CSA) devoted to segmentation
                 applications. The main contribution of our work is to
                 adapt Darwinian evolutionary theory with heuristic CSA.
                 First, the population is divided into specified groups
                 and each group tries to find better location in the
                 search space. A policy of encouragement and punishment
                 is set on searching agents to avoid being trapped in
                 the local optimum and premature solutions. Moreover, to
                 increase the convergence rate of the proposed method, a
                 gray-scale map is applied to out-boundary agents. Ten
                 test images are selected to measure the ability of our
                 algorithm, compared with the famous procedure, energy
                 curve method. Two popular entropies i.e. Otsu and Kapur
                 are employed to evaluate the capability of the
                 introduced algorithm. Eight different search algorithms
                 are implemented and compared to the introduced method.
                 The obtained results show that our method, compared
                 with the original CSA, and other heuristic search
                 methods, can extract multi-level thresholding more
                 efficiently.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Boucetta:2022:BAU,
  author =       "Aldjia Boucetta and Leila Boussaad",
  title =        "Biometric Authentication Using Finger-Vein Patterns
                 with Deep-Learning and Discriminant Correlation
                 Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500139",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Feb 9 07:11:50 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500139",
  abstract =     "Finger-vein identification, a biometric technology
                 that uses vein patterns in the human finger to identify
                 people. In recent years, it has received increasing
                 attention due to its tremendous advantages compared to
                 fingerprint characteristics. Moreover,
                 Deep-Convolutional Neural Networks (Deep-CNN) appeared
                 to be highly successful for feature extraction in the
                 finger-vein area, and most of the proposed works focus
                 on new Convolutional Neural Network (CNN) models, which
                 require huge databases for training, a solution that
                 may be more practicable in real world applications, is
                 to reuse pretrained Deep-CNN models. In this paper, a
                 finger-vein identification system is proposed, which
                 uses Squeezenet pretrained Deep-CNN model as feature
                 extractor from the left and the right finger vein
                 patterns. Then, combines this Deep-based features by
                 using a feature-level Discriminant Correlation Analysis
                 (DCA) to reduce feature dimensions and to give the most
                 relevant features. Finally, these composite feature
                 vectors are used as input data for a Support Vector
                 Machine (SVM) classifier, in an identification stage.
                 This method is tested on two widely available finger
                 vein databases, namely SDUMLA-HMT and FV-USM.
                 Experimental results show that the proposed finger vein
                 identification system achieves significant high mean
                 accuracy rates.",
  acknowledgement = ack-nhfb,
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gajabe:2022:SKB,
  author =       "Rajashree Gajabe and Syed Taqi Ali",
  title =        "Secret Key-Based Image Steganography in Spatial
                 Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467822500140",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500140",
  abstract =     "Day by day, the requirement for secure communication
                 among users is rising in a digital world, to protect
                 the message from the undesirable users. Steganography
                 is a methodology that satisfies the user's necessity of
                 secure communication by inserting a message into
                 different formats. This paper proposes a secret
                 key-based image steganography to secure the message by
                 concealing the grayscale image inside a cover image.
                 The proposed technique shares the 20 characters long
                 secret key between two clients where the initial eight
                 characters of a secret key are utilized for bit
                 permutation of characters and pixels while the last 12
                 characters of secret key decide the encryption keys and
                 position of pixels of a grayscale image into the cover.
                 The grayscale image undergoes operation such as
                 encryption and chaotic baker followed by its hiding in
                 a cover to form a stego image. The execution of the
                 proposed strategy is performed on Matlab 2018. It shows
                 that the proposed approach manages to store the maximum
                 message of size 16 KB into the cover of size
                 256{\texttimes}256. The image quality of stego images
                 has been evaluated using PSNR, MSE. For a full payload
                 of 16 KB, PSNR is around 51 dB to 53 dB which is
                 greater than satisfactory PSNR.",
  acknowledgement = ack-nhfb,
  articleno =    "2250014",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gagaoua:2022:HMB,
  author =       "Meriem Gagaoua and Hamza Ghilas and Abdelkamel Tari
                 and Mohamed Cheriet",
  title =        "Histogram of Marked Background {(HMB)} Feature
                 Extraction Method for {Arabic} Handwriting
                 Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500152",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500152",
  abstract =     "Features extraction is one of the most important steps
                 in handwriting recognition systems. In this paper, we
                 propose a novel features extraction method, which is
                 adapted to the complex nature of Arabic handwriting.
                 The proposed feature called histogram of marked
                 background (HMB) is not considering only ink pixels in
                 a text image, but also uses the background of the
                 image. Each background pixel in the text image was
                 marked according to the repartition of ink pixels in
                 its neighborhood. Feature vectors are extracted by
                 computing histograms from the marked images. Hidden
                 Markov models (HMMs) with Hidden Markov model toolkit
                 (HTK) were used in the recognition process. The
                 experiments were performed on two datasets: IBN SINA
                 database of historical Arabic documents and Isolated
                 Farsi Handwritten Character Database (IFHCDB). The
                 proposed feature in this study produced efficient and
                 promising results for Arabic handwriting recognition,
                 for both isolated characters and for historical
                 documents.",
  acknowledgement = ack-nhfb,
  articleno =    "2250015",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rashwan:2022:MFW,
  author =       "Shaheera Rashwan and Walaa Sheta",
  title =        "A Metaheuristics Framework for Weighted Multi-band
                 Image Fusion",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500164",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500164",
  abstract =     "The main objective of hyper/multispectral image fusion
                 is producing a composite color image that allows for an
                 appropriate visualization of the relevant spatial and
                 spectral information. In this paper, we propose a
                 general framework for spectral weighting-based image
                 fusion. The proposed methodology relies on weight
                 updates conducted using nature-inspired algorithms and
                 a goodness-of-fit criterion defined as the average root
                 mean square error. Simulations on four public data sets
                 and a recent Landsat 8 image of Brullus Lake, Egypt, as
                 an area of study prove the efficiency of the proposed
                 framework. The purpose of the study is to present a
                 framework of multi-band image fusion that produces a
                 fused image of high quality to be further used in
                 computer processing and the results show that the image
                 produced by the presented framework has the highest
                 quality compared with some of the state-of-the art
                 algorithms. To prove the increase in the image quality,
                 we used general quality metrics such as Universal Image
                 Quality Index, Mutual Information, the Variance and
                 Information Measure.",
  acknowledgement = ack-nhfb,
  articleno =    "2250016",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Vaidya:2022:HEM,
  author =       "Bhaumik Vaidya and Chirag Paunwala",
  title =        "Hardware Efficient Modified {CNN} Architecture for
                 Traffic Sign Detection and Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500176",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500176",
  abstract =     "Traffic sign recognition is a vital part for any
                 driver assistance system which can help in making
                 complex driving decision based on the detected traffic
                 signs. Traffic sign detection (TSD) is essential in
                 adverse weather conditions or when the vehicle is being
                 driven on the hilly roads. Traffic sign recognition is
                 a complex computer vision problem as generally the
                 signs occupy a very small portion of the entire image.
                 A lot of research is going on to solve this issue
                 accurately but still it has not been solved till the
                 satisfactory performance. The goal of this paper is to
                 propose a deep learning architecture which can be
                 deployed on embedded platforms for driver assistant
                 system with limited memory and computing resources
                 without sacrificing on detection accuracy. The
                 architecture uses various architectural modification to
                 the well-known Convolutional Neural Network (CNN)
                 architecture for object detection. It uses a trainable
                 Color Transformer Network (CTN) with the existing CNN
                 architecture for making the system invariant to
                 illumination and light changes. The architecture uses
                 feature fusion module for detecting small traffic signs
                 accurately. In the proposed work, receptive field
                 calculation is used for choosing the number of
                 convolutional layer for prediction and the right scales
                 for default bounding boxes. The architecture is
                 deployed on Jetson Nano GPU Embedded development board
                 for performance evaluation at the edge and it has been
                 tested on well-known German Traffic Sign Detection
                 Benchmark (GTSDB) and Tsinghua-Tencent 100k dataset.
                 The architecture only requires 11 MB for storage which
                 is almost ten times better than the previous
                 architectures. The architecture has one sixth
                 parameters than the best performing architecture and 50
                 times less floating point operations per second
                 (FLOPs). The architecture achieves running time of 220
                 ms on desktop GPU and 578 ms on Jetson Nano which is
                 also better compared to other similar implementation.
                 It also achieves comparable accuracy in terms of mean
                 average precision (mAP) for both the datasets.",
  acknowledgement = ack-nhfb,
  articleno =    "2250017",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bommisetty:2022:CBV,
  author =       "Reddy Mounika Bommisetty and Ashish Khare and Manish
                 Khare and P. Palanisamy",
  title =        "Content-Based Video Retrieval Using Integration of
                 Curvelet Transform and Simple Linear Iterative
                 Clustering",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500188",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500188",
  abstract =     "Video is a rich information source containing both
                 audio and visual information along with motion
                 information embedded in it. Applications such as
                 e-learning, live TV, video on demand, traffic
                 monitoring, etc. need an efficient video retrieval
                 strategy. Content-based video retrieval and superpixel
                 segmentation are two diverse application areas of
                 computer vision. In this work, we are presenting an
                 algorithm for content-based video retrieval with help
                 of Integration of Curvelet transform and Simple Linear
                 Iterative Clustering (ICTSLIC) algorithm. Proposed
                 algorithm consists of two steps: off line processing
                 and online processing. In offline processing, keyframes
                 of the database videos are extracted by employing
                 features: Pearson Correlation Coefficient (PCC) and
                 color moments (CM) and on the extracted keyframes
                 superpixel generation algorithm ICTSLIC is applied. The
                 superpixels generated by applying ICTSLIC on keyframes
                 are used to represent database videos. On other side,
                 in online processing, ICTSLIC superpixel segmentation
                 is applied on query frame and the superpixels generated
                 by segmentation are used to represent query frame. Then
                 videos similar to query frame are retrieved through
                 matching done by calculation of Euclidean distance
                 between superpixels of query frame and database
                 keyframes. Results of the proposed method are
                 irrespective of query frame features such as camera
                 motion, object's pose, orientation and motion due to
                 the incorporation of ICTSLIC superpixels as base
                 feature for matching and retrieval purpose. The
                 proposed method is tested on the dataset comprising of
                 different categories of video clips such as animations,
                 serials, personal interviews, news, movies and songs
                 which is publicly available. For evaluation, the
                 proposed method randomly picks frames from database
                 videos, instead of selecting keyframes as query frames.
                 Experiments were conducted on the developed dataset and
                 the performance is assessed with different parameters
                 Precision, Recall, Jaccard Index, Accuracy and
                 Specificity. The experimental results shown that the
                 proposed method is performing better than the other
                 state-of-art methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2250018",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Khan:2022:MPS,
  author =       "Rafflesia Khan and Rameswar Debnath",
  title =        "Morphology Preserving Segmentation Method for Occluded
                 Cell Nuclei from Medical Microscopy Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S021946782250019X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782250019X",
  abstract =     "Nowadays, image segmentation techniques are being used
                 in many medical applications such as tissue culture
                 monitoring, cell counting, automatic measurement of
                 organs, etc., for assisting doctors. However,
                 high-level segmentation results cannot be obtained
                 without manual annotation or prior knowledge for high
                 variability, noise and other imaging artifacts in
                 medical images. Furthermore, unstable and continuously
                 changing characteristics of all human cells, tissues
                 and organs manipulate training-based segmentation
                 methods. Detecting appropriate contour of a region of
                 interest and single cells from overlapping condition
                 are extremely challenging. In this paper, we aim for a
                 model that can detect biological structure (e.g. cell
                 nuclei and lung contour) with their proper morphology
                 even in overlapping or occluded condition without
                 manual annotation or prior knowledge. We have
                 introduced a new optimal approach for automatic medical
                 image region segmentation. The method first clearly
                 focuses the boundaries of all object regions in a
                 microscopy image. Then it detects the areas by
                 following their contours. Our model is capable of
                 detecting and segmenting object regions from medial
                 image using less computation effort. Our experimental
                 results prove that our model provides better detection
                 on several datasets of different types of medical data
                 and ensures more than 98\% segmentation rate in the
                 case of densely connected regions.",
  acknowledgement = ack-nhfb,
  articleno =    "2250019",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tanveer:2022:EIP,
  author =       "Muhammad Tanveer and Tariq Shah and Asif Ali and
                 Dawood Shah",
  title =        "An Efficient Image Privacy-Preserving Scheme Based On
                 Mixed Chaotic Map and Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500206",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500206",
  abstract =     "In the digital modern era, multimedia security has
                 turned into a major concern by the rapid growth of
                 network technologies and digital communications.
                 Accordingly, from the last few decades, the application
                 of nonlinear dynamics and chaotic phenomena for
                 multimedia data security earn significant attention. In
                 this paper, an efficient image-encryption technique
                 based on a two-dimensional (2D) chaotic system combine
                 with the finite field of the specific order is
                 introduced. The proposed scheme consists of four
                 modules which are the separation of bits, compression,
                 2D chaotic map, and small S-boxes. Initially, the
                 suggested scheme separates the pixels of the image into
                 the least significant bits (LSB) and the most
                 significant bits (MSB). Subsequently, the compression
                 algorithm on these separated bits is applied and
                 instantly transformed the MSB of the image into LSB.
                 The key objective of the first module is to minimize
                 the range of the pixel value up to eight times less
                 than the original image, which consequently reduces the
                 time complexity of the scheme. In the end, a 2D chaotic
                 map is used to reshuffle the bytes to interrupt the
                 internal correlation amongst the pixels of the image.
                 At the tail end, the small S-boxes have been used to
                 substitute the permuted image. The significance of
                 small S-boxes plays a vital role to maintain the
                 optimum security level, prevent computational effort,
                 and reduced time complexity. The result of the
                 suggested encryption system is tailor-made for
                 instantaneous communication.",
  acknowledgement = ack-nhfb,
  articleno =    "2250020",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chaudhari:2022:IFU,
  author =       "Chaitrali Prasanna Chaudhari and Satish Devane",
  title =        "Improved Framework using Rider Optimization Algorithm
                 for Precise Image Caption Generation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500218",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500218",
  abstract =     "``Image Captioning is the process of generating a
                 textual description of an image''. It deploys both
                 computer vision and natural language processing for
                 caption generation. However, the majority of the image
                 captioning systems offer unclear depictions regarding
                 the objects like ``man'', ``woman'', ``group of
                 people'', ``building'', etc. Hence, this paper intends
                 to develop an intelligent-based image captioning model.
                 The adopted model comprises of few steps like word
                 generation, sentence formation, and caption generation.
                 Initially, the input image is subjected to the Deep
                 learning classifier called Convolutional Neural Network
                 (CNN). Since the classifier is already trained in the
                 relevant words that are related to all images, it can
                 easily classify the associated words of the given
                 image. Further, a set of sentences is formed with the
                 generated words using Long-Short Term Memory (LSTM)
                 model. The likelihood of the formed sentences is
                 computed using the Maximum Likelihood (ML) function,
                 and the sentences with higher probability are taken,
                 which is further used for generating the visual
                 representation of the scene in terms of image caption.
                 As a major novelty, this paper aims to enhance the
                 performance of CNN by optimally tuning its weight and
                 activation function. This paper introduces a new
                 enhanced optimization algorithm Rider with Randomized
                 Bypass and Over-taker update (RR-BOU) for this optimal
                 selection. In the proposed RR-BOU is the enhanced
                 version of the Rider Optimization Algorithm (ROA).
                 Finally, the performance of the proposed captioning
                 model is compared over other conventional models with
                 respect to statistical analysis.",
  acknowledgement = ack-nhfb,
  articleno =    "2250021",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hamroun:2022:MVI,
  author =       "Mohamed Hamroun and Karim Tamine and Beno{\^\i}t
                 Crespin",
  title =        "Multimodal Video Indexing {(MVI)}: a New Method Based
                 on Machine Learning and Semi-Automatic Annotation on
                 Large Video Collections",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S021946782250022X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782250022X",
  abstract =     "Indexing video by the concept is one of the most
                 appropriate solutions for such problems. It is based on
                 an association between a concept and its corresponding
                 visual sound, or textual features. This kind of
                 association is not a trivial task. It requires
                 knowledge about the concept and its context. In this
                 paper, we investigate a new concept detection approach
                 to improve the performance of content-based multimedia
                 documents retrieval systems. To achieve this goal, we
                 are going to tackle the problem from different plans
                 and make four contributions at various stages of the
                 indexing process. We propose a new method for
                 multimodal indexation based on (i) a new weakly
                 supervised semi-automatic method based on the genetic
                 algorithm (ii) the detection of concepts from the text
                 in the videos (iii) the enrichment of the basic
                 concepts thanks to the usage of our method DCM.
                 Subsequently, the semantic and enriched concepts allow
                 a better multimodal indexation and the construction of
                 an ontology. Finally, the different contributions are
                 tested and evaluated on a large dataset (TRECVID
                 2015).",
  acknowledgement = ack-nhfb,
  articleno =    "2250022",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shivaprasad:2022:ABT,
  author =       "B. J. Shivaprasad and M. Ravikumar and D. S. Guru",
  title =        "Analysis of Brain Tumor Using {MR} Images: a Brief
                 Survey",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500231",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500231",
  abstract =     "In this paper, we have discussed in detail about
                 detection and extraction of brain tumor from MRI
                 technique, where the importance of using MRI is also
                 highlighted. Various features extraction methods and
                 classifiers are explained in brain tumor segmentation.
                 This paper mainly focuses on challenges involved in
                 brain tumor analysis, which is helpful for researchers
                 and those who are interested to carry out their
                 research on this topic.",
  acknowledgement = ack-nhfb,
  articleno =    "2250023",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Khadilkar:2022:CCD,
  author =       "Samrat Pundalik Khadilkar",
  title =        "Colon Cancer Detection Using Hybrid Features and
                 Genetically Optimized Neural Network Classifier",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500243",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500243",
  abstract =     "Computer-assisted colon cancer detection on the
                 histopathological images has become a tedious task due
                 to its shape characteristics and other biological
                 properties. The images acquired through the
                 histopathological microscope may vary in magnifications
                 for better visibility. This may change the
                 morphological properties and hence an automated
                 magnification independent colon cancer detection system
                 is essential. The manual diagnosis of colon biopsy
                 images is subjective, sluggish, laborious leading to
                 nonconformity between histopathologists due to visual
                 evaluation at various microscopic magnifications.
                 Automatic detection of colon across image
                 magnifications is challenging due to many aspects like
                 tailored segmentation and varying features. This
                 demands techniques that take advantage of the textural,
                 color, and geometric properties of colon tissue. This
                 work exhibits a segmentation approach based on the
                 morphological features derived from the segmented
                 region. Gabor Wavelet, Harris Corner, and DWT-LBP
                 coefficients are extracted as it should not be
                 dependent on the spatial domain with respect to the
                 magnification. These features are fed to the
                 Genetically Optimized Neural Network classifier to
                 classify them as normal and malignant ones. Here, the
                 genetic algorithm is used to learn the best
                 hyper-parameters for a neural network.",
  acknowledgement = ack-nhfb,
  articleno =    "2250024",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sridhar:2022:PAT,
  author =       "S. Sridhar and A. Kalaivani",
  title =        "Performance Analysis of Two-Stage Iterative Ensemble
                 Method over Random Oversampling Methods on Multiclass
                 Imbalanced Datasets",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500255",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500255",
  abstract =     "Data imbalance occurring among multiclass datasets is
                 very common in real-world applications. Existing
                 studies reveal that various attempts were made in the
                 past to overcome this multiclass imbalance problem,
                 which is a severe issue related to the typical
                 supervised machine learning methods such as
                 classification and regression. But, still there exists
                 a need to handle the imbalance problem efficiently as
                 the datasets include both safe and unsafe minority
                 samples. Most of the widely used oversampling
                 techniques like SMOTE and its variants face challenges
                 in replicating or generating the new data instances for
                 balancing them across multiple classes, particularly
                 when the imbalance is high and the number of rare
                 samples count is too minimal thus leading the
                 classifier to misclassify the data instances. To lessen
                 this problem, we proposed a new data balancing method
                 namely a two-stage iterative ensemble method to tackle
                 the imbalance in multiclass environment. The proposed
                 approach focuses on the rare minority sample's
                 influence on learning from imbalanced datasets and the
                 main idea of the proposed approach is to balance the
                 data without any change in class distribution before it
                 gets trained by the learner such that it improves the
                 learner's learning process. Also, the proposed approach
                 is compared against two widely used oversampling
                 techniques and the results reveals that the proposed
                 approach shows a much significant improvement in the
                 learning process among the multiclass imbalanced
                 data.",
  acknowledgement = ack-nhfb,
  articleno =    "2250025",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jayaraman:2022:MFP,
  author =       "Kumaran @ Kumar Jayaraman and Koganti Srilakshmi and
                 Sasikala Jayaraman",
  title =        "Modified Flower Pollination-based Segmentation of
                 Medical Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "02",
  pages =        "??--??",
  month =        apr,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500267",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri May 6 07:27:02 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500267",
  abstract =     "This paper presents a modified flower
                 pollination-based method for performing multilevel
                 segmentation of medical images. The flower
                 pollination-based optimization (FPO) models the
                 pollination process of flowers. Bees serve a major role
                 in the pollination activity of flowers and they
                 memorize and recognize the best flowers producing large
                 pollens of nectar. Such memorizing ability of bees is
                 adapted in the FPO for improving the exploration
                 ability of the algorithm. Besides, the mechanism of
                 avoiding predators by pollinators is also included in
                 the modified FPO (MFPO) for getting away from
                 sub-optimal traps. The medical image segmentation
                 problem is transformed into an optimization problem and
                 solved using the modified FPO (MFPO). The method
                 explores for optimal thresholds in the problem space of
                 the given medical image. The segmented images are
                 presented for showing the superior performance of the
                 proposed method.",
  acknowledgement = ack-nhfb,
  articleno =    "2250026",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Patel:2022:AET,
  author =       "Alpesh M. Patel and Anil Suthar",
  title =        "{AdaBoosted} Extra Trees Classifier for Object-Based
                 Multispectral Image Classification of Urban Fringe
                 Area",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467821400064",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400064",
  abstract =     "In the past decade, it is proven that satellite image
                 classification using an object-based technique is
                 better than the standard pixel-based technique. With
                 the increasing need for classifying multispectral
                 satellite images for urban planning, the accuracy of
                 the classification becomes a significant performance
                 parameter. Object-based classification (OBC) is a
                 technique in which group of pixels having similar
                 spectral properties, called objects, are generated
                 using image segmentation and then these objects are
                 classified based on their attributes. In this paper,
                 the combination of a multiclass AdaBoost algorithm with
                 extra trees classifier (ETC) is proposed with higher
                 prediction accuracy for the OBC of the urban fringe
                 area. The performance of the AdaBoost algorithm is
                 found to be better in terms of classification accuracy
                 than benchmarked SVM and RF classifiers for OBC. These
                 classification methods were applied to IRS-R2 LISS IV
                 data. The AdaBoosted extra trees classifier (ABETC) has
                 demonstrated the highest accuracy with overall accuracy
                 (OA) of 88.47\% and a kappa coefficient of 0.85. The
                 computational time of the ABETC is found to be much
                 smaller than the RF algorithm. In detail, the
                 sensitivity of the classifiers was investigated using
                 stratified random sampling with various sample sizes.",
  acknowledgement = ack-nhfb,
  articleno =    "2140006",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Hans:2022:HBB,
  author =       "Rahul Hans and Harjot Kaur",
  title =        "Hybrid Biogeography-Based Optimization and Genetic
                 Algorithm for Feature Selection in Mammographic Breast
                 Density Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467821400076",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400076",
  abstract =     "It can be acknowledged from the literature that the
                 high density of breast tissue is a root cause for the
                 escalation of breast cancer among the women, imparting
                 its prime role in Cancer Death among women. Moreover,
                 in this era where computer-aided diagnosis systems have
                 become the right hand of the radiologists, the
                 researchers still find room for improvement in the
                 feature selection techniques. This research aspires to
                 propose hybrid versions of Biogeography-Based
                 Optimization and Genetic Algorithm for feature
                 selection in Breast Density Classification, to get rid
                 of redundant and irrelevant features from the dataset;
                 along with it to achieve the superior classification
                 accuracy or to uphold the same accuracy with lesser
                 number of features. For experimentation, 322 mammogram
                 images from mini-MIAS database are chosen, and then
                 Region of Interests (ROI) of seven different sizes are
                 extracted to extract a set of 45 texture features
                 corresponding to each ROI. Subsequently, the proposed
                 algorithms are used to extract an optimal subset of
                 features from the hefty set of features corresponding
                 to each ROI. The results indicate the outperformance of
                 the proposed algorithms when results were compared with
                 some of the other nature-inspired metaheuristic
                 algorithms using various parameters.",
  acknowledgement = ack-nhfb,
  articleno =    "2140007",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Sagayam:2022:RHG,
  author =       "K. Martin Sagayam and A. Diana Andrushia and Ahona
                 Ghosh and Omer Deperlioglu and Ahmed A. Elngar",
  title =        "Recognition of Hand Gesture Image Using Deep
                 Convolutional Neural Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467821400088",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400088",
  abstract =     "In recent technology, there is tremendous growth in
                 computer applications that highlight human--computer
                 interaction (HCI), such as augmented reality (AR), and
                 Internet of Things (IoT). As a consequence, hand
                 gesture recognition was highlighted as a very
                 up-to-date research area in computer vision. The body
                 language is a vital method to communicate between
                 people, as well as emphasis on voice messages, or as a
                 complete message on its own. Thus, automatic hand
                 gestures recognition systems can be used to increase
                 human--computer interaction. Therefore, many approaches
                 for hand gesture recognition systems have been
                 designed. However, most of these methods include hybrid
                 processes such as image pre-processing, segmentation,
                 and classification. This paper describes how to create
                 hand gesture model easily and quickly with a well-tuned
                 deep convolutional neural network. Experiments were
                 performed using the Cambridge Hand Gesture data set for
                 illustration of success and efficiency of the
                 convolutional neural network. The accuracy was achieved
                 as 96.66\%, where sensitivity and specificity were
                 found to be 85\% and 98.12\%, respectively, according
                 to the average values obtained at the end of 20 times
                 of operation. These results were compared with the
                 existing works using the same dataset and it was found
                 to have higher values than the hybrid methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2140008",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Singh:2022:EMD,
  author =       "Swati Singh and Sheifali Gupta and Ankush Tanta and
                 Rupesh Gupta",
  title =        "Extraction of Multiple Diseases in Apple Leaf Using
                 Machine Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S021946782140009X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782140009X",
  abstract =     "This paper proposes a novel algorithm of segmentation
                 of diseased part in apple leaf images. In
                 agriculture-based image processing, leaf diseases
                 segmentation is the main processing task for region of
                 interest extraction. It is also extremely important to
                 segment the plant leaf from the background in case on
                 live images. Automated segmentation of plant leaves
                 from the background is a common challenge in the
                 processing of plant images. Although numerous methods
                 have been proposed, still it is tough to segment the
                 diseased part of the leaf from the live leaf images
                 accurately by one particular method. In the proposed
                 work, leaves of apple having different background have
                 been segmented. Firstly, the leaves have been enhanced
                 by using Brightness-Preserving Dynamic Fuzzy Histogram
                 Equalization technique and then the extraction of
                 diseased apple leaf part is done using a novel
                 extraction algorithm. Real-time plant leaf database is
                 used to validate the proposed approach. The results of
                 the proposed novel methodology give better results when
                 compared to existing segmentation algorithms. From the
                 segmented apple leaves, color and texture features are
                 extracted which are further classified as marsonina
                 coronaria or apple scab using different machine
                 learning classifiers. Best accuracy of 96.4\% is
                 achieved using K nearest neighbor classifier.",
  acknowledgement = ack-nhfb,
  articleno =    "2140009",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Gupta:2022:SSC,
  author =       "Anuj Kumar Gupta and Manvinder Sharma and Ankit Sharma
                 and Vikas Menon",
  title =        "A Study on {SARS-CoV-2 (COVID-19)} and Machine
                 Learning Based Approach to Detect {COVID-19} Through
                 {X}-Ray Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467821400106",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400106",
  abstract =     "From origin in Wuhan city of China, a highly
                 communicable and deadly virus is spreading in the
                 entire world and is known as COVID-19. COVID-19 is a
                 new species of coronavirus which is affecting
                 respiratory system of human. The virus is known as
                 severe acute respiratory syndrome (SARS) coronavirus 2
                 abbreviated as SARS-CoV-2 and generally known as
                 coronavirus disease COVID-19. This is growing day by
                 day in countries. The symptoms include fever, cough and
                 difficulty in breathing. As there is no vaccine made
                 for this virus and COVID-19 tests are not readily
                 available, this is causing panic. Various Artificial
                 Intelligence-based algorithms and frameworks are being
                 developed to detect this virus, but it has not been
                 tested. People are taking advantages of others by
                 providing duplicate COVID-19 test kits. A work is
                 carried out with deep learning to detect presence of
                 COVID 19. With the use of Convolutional Neural
                 networks, the model is trained with dataset of COVID-19
                 positive and negative X-Rays. The accuracy of training
                 model is 99\% and the confusion matrix shows 98\%
                 values that are predicted truly. Hence, the model is
                 able to detect the presence of COVID-19.",
  acknowledgement = ack-nhfb,
  articleno =    "2140010",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Rani:2022:SIP,
  author =       "Rajneesh Rani and Renu Dhir and Deepti Kakkar and
                 Nonita Sharma",
  title =        "Script Identification for Printed and Handwritten
                 {Indian} Documents: an Empirical Study of Different
                 Feature Classifier Combinations",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467821400118",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400118",
  abstract =     "The identification of script in a document page image
                 is the first step for an OCR system processing
                 multi-script documents. In this
                 multilingual/multiscript world, document processing
                 systems relying on the OCR that need human involvement
                 to select the appropriate OCR package is definitely
                 undesirable and inefficient. The development of robust
                 and efficient methods for automatic script
                 identification of a document is a subject of major
                 importance for automatic document processing in a
                 multilingual/multiscript environment. Thus, the basic
                 objective is to come up with some intuitive methods
                 having straightforward implementation without
                 compromising with efficiency. The aim of this work is
                 to evaluate state-of-the-art feature extraction and
                 classification techniques in the field of automatic
                 script identification of printed and handwritten
                 documents and to propose the best combination for the
                 same.",
  acknowledgement = ack-nhfb,
  articleno =    "2140011",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Kaur:2022:DAD,
  author =       "Swapandeep Kaur and Sheifali Gupta and Swati Singh and
                 Isha Gupta",
  title =        "Detection of {Alzheimer}'s Disease Using Deep
                 Convolutional Neural Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S021946782140012X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782140012X",
  abstract =     "Alzheimer's disease (AD) is a disease that gradually
                 develops and causes degeneration of the cells of the
                 brain. The leading cause of AD is dementia that results
                 in a person's inability to work independently. In the
                 early stages of AD, a person forgets recent
                 conversations or the occurrence of an event. In the
                 later stages, there could be severe loss of memory such
                 that the person is not able to even perform everyday
                 tasks. The medicines currently available for AD may
                 improve its symptoms on a temporary basis in the early
                 stage of the disease. Since no treatment is available
                 for curing AD, its detection becomes extremely
                 important. As the clinical treatments are very
                 expensive, the need for automated diagnosis of AD is of
                 critical importance. In this paper, a deep learning
                 model based on a convolutional neural network has been
                 used and applied to four classes of images of AD that
                 is very mild demented, mild demented, average demented,
                 and non-demented. It was found that the moderate
                 demented class had the highest accuracy of 98.9\%, a
                 classification error rate of 0.01, and a specificity of
                 0.992. Also, the lowest false positive rate of 0.007
                 was obtained.",
  acknowledgement = ack-nhfb,
  articleno =    "2140012",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Gore:2022:IBR,
  author =       "Sonal Gore and Jayant Jagtap",
  title =        "{IDH}-Based Radiogenomic Characterization of Glioma
                 Using Local Ternary Pattern Descriptor Integrated with
                 Radiographic Features and Random Forest Classifier",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467821400131",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400131",
  abstract =     "Mutations in family of Isocitrate Dehydrogenase (IDH)
                 gene occur early in oncogenesis, especially with glioma
                 brain tumor. Molecular diagnostic of glioma using
                 machine learning has grabbed attention to some extent
                 from last couple of years. The development of
                 molecular-level predictive approach carries great
                 potential in radiogenomic field. But more focused
                 efforts need to be put to develop such approaches. This
                 study aims to develop an integrative genomic diagnostic
                 method to assess the significant utility of textures
                 combined with other radiographic and clinical features
                 for IDH classification of glioma into IDH mutant and
                 IDH wild type. Random forest classifier is used for
                 classification of combined set of clinical features and
                 radiographic features extracted from axial T2-weighted
                 Magnetic Resonance Imaging (MRI) images of low- and
                 high-grade glioma. Such radiogenomic analysis is
                 performed on The Cancer Genome Atlas (TCGA) data of 74
                 patients of IDH mutant and 104 patients of IDH wild
                 type. Texture features are extracted using uniform,
                 rotation invariant Local Ternary Pattern (LTP) method.
                 Other features such as shape, first-order statistics,
                 image contrast-based, clinical data like age,
                 histologic grade are combined with LTP features for IDH
                 discrimination. Proposed random forest-assisted model
                 achieved an accuracy of 85.89\% with multivariate
                 analysis of integrated set of feature descriptors using
                 Glioblastoma and Low-Grade Glioma dataset available
                 with The Cancer Imaging Archive (TCIA). Such an
                 integrated feature analysis using LTP textures and
                 other descriptors can effectively predict molecular
                 class of glioma as IDH mutant and wild type.",
  acknowledgement = ack-nhfb,
  articleno =    "2140013",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Singh:2022:OAI,
  author =       "Rishipal Singh and Rajneesh Rani and Aman Kamboj",
  title =        "An Optimized Approach for Intra-Class Fruit
                 Classification Using Deep Convolutional Neural
                 Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467821400143",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue May 31 06:44:45 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467821400143",
  abstract =     "Fruits classification is one of the influential
                 applications of computer vision. Traditional
                 classification models are trained by considering
                 various features such as color, shape, texture, etc.
                 These features are common for different varieties of
                 the same fruit. Therefore, a new set of features is
                 required to classify the fruits belonging to the same
                 class. In this paper, we have proposed an optimized
                 method to classify intra-class fruits using deep
                 convolutional layers. The proposed architecture is
                 capable of solving the challenges of a commercial
                 tray-based system in the supermarket. As the research
                 in intra-class classification is still in its infancy,
                 there are challenges that have not been tackled. So,
                 the proposed method is specifically designed to
                 overcome the challenges related to intra-class fruits
                 classification. The proposed method showcases an
                 impressive performance for intra-class classification,
                 which is achieved using a few parameters than the
                 existing methods. The proposed model consists of
                 Inception block, Residual connections and various other
                 layers in very precise order. To validate its
                 performance, the proposed method is compared with
                 state-of-the-art models and performs best in terms of
                 accuracy, loss, parameters, and depth.",
  acknowledgement = ack-nhfb,
  articleno =    "2140014",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Innovations in Image Processing using
                 Machine Learning",
}

@Article{Zhang:2022:MDM,
  author =       "Qi Zhang",
  title =        "Medical Data and Mathematically Modeled Implicit
                 Surface Real-Rime Visualization in {Web} Browsers",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467822500279",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500279",
  abstract =     "Raycasting can display volumetric medical data in fine
                 details and reveal crucial inner imaging information,
                 while implicit surface is able to effectively model
                 complex objects with high flexibility, combining these
                 two rendering modalities together will provide
                 comprehensive information of the scene and has wide
                 applications in surgical simulation, image-guided
                 intervention, and medical training. However, medical
                 data rendering is based on texture depth at every
                 sampling point, while mathematically modeled implicit
                 surfaces do not have geometric information in texture
                 space. It is a challenging task to visualize both
                 physical scalar data and virtual implicit surfaces
                 simultaneously. To address this issue, in this paper,
                 we present a new dual-casting ray-based double modality
                 data rendering algorithm and web-based software
                 platform to visualize volumetric medical data and
                 implicit surface in the same browser. The algorithm
                 runs on graphics processing unit and casts two virtual
                 rays from camera to each pixel on the display panel,
                 where one ray travels through the mathematically
                 defined scene for implicit surface rendering and the
                 other one passes the 3D texture space for volumetric
                 data visualization. The proposed algorithm can detect
                 voxel depth information and algebraic surface models
                 along each casting ray and dynamically enhance the
                 visualized dual-modality data with the improved
                 lighting model and transparency adjustment function.
                 Moreover, auxiliary innovative techniques are also
                 presented to enhance the shading and rendering features
                 of interest. Our software platform can seamlessly
                 visualize volumetric medical data and implicit surfaces
                 in the same web browser over Internet.",
  acknowledgement = ack-nhfb,
  articleno =    "2250027",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shah:2022:NRI,
  author =       "Said Khalid Shah",
  title =        "Non-Rigid Image Registration based on Parameterized
                 Surfaces: Application to {$3$D} Cardiac Motion Image
                 Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500280",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500280",
  abstract =     "This paper describes the Fast Radial Basis Function
                 (RBF) method for cardiac motion tracking in 3D CT using
                 non-rigid medical image registration based on
                 parameterized (regular) surfaces. The technique is a
                 point-based registration evaluation algorithm which
                 does register 3D MR or CT images in real time. We first
                 extract the surface of the whole heart 3D CT and its
                 contrast enhanced part (left ventricle (LV) blood
                 cavity) of each dataset with a semiautomatic contouring
                 and a fully-automatic triangulation method followed by
                 a global surface parameterization and optimization
                 algorithm. In second step, a set of registration
                 experiments are run to calculate the deformation field
                 at various phases of cardiac motion or cycle from CT
                 images, which results into significant deformation
                 during each phase of a cycle. The surface points of the
                 whole heart and LV are used to register the source
                 systole image to various diastole target images taken
                 at different phases during a heart beat. Our
                 registration accuracy improves with the increase in
                 number of salient feature points (i.e. optimized
                 parameterized surfaces) and it has no effect on the
                 speed of the algorithm (i.e. still less than a second).
                 The results show that the target registration error is
                 less than 3 mm (2.53) and the performance of the Fast
                 RBF algorithm is less than a second using a whole heart
                 CT dataset of a single patient taken over the course of
                 the entire cardiac cycle. At the end, the results for
                 recovery (or analysis) of bigger deformation in heart
                 CT images using the Fast RBF algorithm is compared to
                 the state-of-the-art Free Form Deformation (FFD)
                 registration technique. It is proved that the Fast RBF
                 method is performing better in speed and slightly less
                 accurate than the FFD (when measured in terms of NMI)
                 due to iterative nature of the latter.",
  acknowledgement = ack-nhfb,
  articleno =    "2250028",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhang:2022:FBC,
  author =       "Geng Zhang and Qi Zhu and Jing Yang and Ruting Xu and
                 Zhiqiang Zhang and Daoqiang Zhang",
  title =        "Functional Brain Connectivity Hyper-Network Embedded
                 with Structural Information for Epilepsy Diagnosis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500292",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500292",
  abstract =     "Automatic diagnosis of brain diseases based on brain
                 connectivity network (BCN) classification is one of the
                 hot research fields in medical image analysis. The
                 functional brain network reflects the brain functional
                 activities and structural brain network reflects the
                 neural connections of the main brain regions. It is of
                 great significance to explore and explain the inner
                 mechanism of the brain and to understand and treat
                 brain diseases. In this paper, based on the graph
                 structure characteristics of brain network, the fusion
                 model of functional brain network and structural brain
                 network is designed to classify the diagnosis of brain
                 mental diseases. Specifically, the main work of this
                 paper is to use the Laplacian graph embed the
                 information of diffusion tensor imaging, which contains
                 the characteristics of structural brain networks, into
                 the functional brain network with hyper-order
                 functional connectivity information built based on
                 functional magnetic resonance data using the sparse
                 representation method, to obtain brain network with
                 both functional and structural characteristics.
                 Projection of the brain network and the two original
                 modes data to the kernel space respectively and then
                 classified by the multi-task learning method.
                 Experiments on the epilepsy dataset show that our
                 method has better performance than several
                 state-of-the-art methods. In addition, brain regions
                 and connections that are highly correlated with disease
                 revealed by our method are discussed.",
  acknowledgement = ack-nhfb,
  articleno =    "2250029",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2022:DSE,
  author =       "Uche A. Nnolim",
  title =        "Dynamic Selective Edge-Based
                 {Integer/Fractional-Order} Partial Differential
                 Equation for Degraded Document Image Binarization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500309",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500309",
  abstract =     "Conventional thresholding algorithms have had limited
                 success with degraded document images. Recently,
                 partial differential equations (PDEs) have been applied
                 with good results. However, these are usually tailored
                 to handle relatively few specific distortions. In this
                 study, we combine an edge detection term with a linear
                 binarization source term in a PDE formulation.
                 Additionally, a new proposed diffusivity function
                 further amplifies desired edges. It also suppresses
                 undesired edges that comprise bleed-through effects.
                 Furthermore, we develop the fractional variant of the
                 proposed scheme, which further improves results and
                 provides more flexibility. Moreover, nonlinear color
                 spaces are utilized to improve binarization results for
                 images with color distortion. The proposed scheme
                 removes document image degradation such as
                 bleed-through, stains, smudges, etc., and also restores
                 faded text in the images. Experimental subjective and
                 objective results show consistently superior
                 performance of the proposed approach compared to the
                 state-of-the-art PDE-based models.",
  acknowledgement = ack-nhfb,
  articleno =    "2250030",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Moradi:2022:IDM,
  author =       "Hamid Moradi and Amir Hossein Foruzan",
  title =        "Integration of Dynamic Multi-Atlas and Deep Learning
                 Techniques to Improve Segmentation of the Prostate in
                 {MR} Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500310",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500310",
  abstract =     "Accurate delineation of the prostate in MR images is
                 an essential step for treatment planning and volume
                 estimation of the organ. Prostate segmentation is a
                 challenging task due to its variable size and shape.
                 Moreover, neighboring tissues have a low-contrast with
                 the prostate. We propose a robust and precise automatic
                 algorithm to define the prostate's boundaries in MR
                 images in this paper. First, we find the prostate's ROI
                 by a deep neural network and decrease the input image's
                 size. Next, a dynamic multi-atlas-based approach
                 obtains the initial segmentation of the prostate. A
                 watershed algorithm improves the initial segmentation
                 at the next stage. Finally, an SSM algorithm keeps the
                 result in the domain of allowable prostate shapes. The
                 quantitative evaluation of 74 prostate volumes
                 demonstrated that the proposed method yields a mean
                 Dice coefficient of 0.83{\textpm}0.05. In comparison
                 with recent researches, our algorithm is robust against
                 shape and size variations.",
  acknowledgement = ack-nhfb,
  articleno =    "2250031",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rajalakshmi:2022:EVP,
  author =       "M. Rajalakshmi and K. Annapurani",
  title =        "Enhancement of Vascular Patterns in Palm Images Using
                 Various Image Enhancement Techniques for Person
                 Identification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500322",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500322",
  abstract =     "Image classification is a complicated process of
                 classifying an image based on its visual
                 representation. This paper portrays the need for
                 adapting and applying a suitable image enhancement and
                 denoising technique in order to arrive at a successful
                 classification of data captured remotely. Biometric
                 properties that are widely explored today are very
                 important for authentication purposes. Noise may be the
                 result of incorrect vein detection in the accepted
                 image, thus explaining the need for a better
                 development technique. This work provides subjective
                 and objective analysis of the performance of various
                 image enhancement filters in the spatial domain. After
                 performing these pre-processing steps, the vein map and
                 the corresponding vein graph can be easily obtained
                 with minimal extraction steps, in which the appropriate
                 Graph Matching method can be used to evaluate hand vein
                 graphs thus performing the person authentication. The
                 analysis result shows that the image enhancement filter
                 performs better as an image enhancement filter compared
                 to all other filters. Image quality measures (IQMs) are
                 also tabulated for the evaluation of image quality.",
  acknowledgement = ack-nhfb,
  articleno =    "2250032",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Abedini:2022:IDU,
  author =       "Maryam Abedini and Horriyeh Haddad and Marzieh Faridi
                 Masouleh and Asadollah Shahbahrami",
  title =        "Image Denoising Using Sparse Representation and
                 Principal Component Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500334",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500334",
  abstract =     "This study proposes an image denoising algorithm based
                 on sparse representation and Principal Component
                 Analysis (PCA). The proposed algorithm includes the
                 following steps. First, the noisy image is divided into
                 overlapped 8{\texttimes}8 blocks. Second, the discrete
                 cosine transform is applied as a dictionary for the
                 sparse representation of the vectors created by the
                 overlapped blocks. To calculate the sparse vector, the
                 orthogonal matching pursuit algorithm is used. Then,
                 the dictionary is updated by means of the PCA algorithm
                 to achieve the sparsest representation of vectors.
                 Since the signal energy, unlike the noise energy, is
                 concentrated on a small dataset by transforming into
                 the PCA domain, the signal and noise can be well
                 distinguished. The proposed algorithm was implemented
                 in a MATLAB environment and its performance was
                 evaluated on some standard grayscale images under
                 different levels of standard deviations of white
                 Gaussian noise by means of peak signal-to-noise ratio,
                 structural similarity indexes, and visual effects. The
                 experimental results demonstrate that the proposed
                 denoising algorithm achieves significant improvement
                 compared to dual-tree complex discrete wavelet
                 transform and K-singular value decomposition image
                 denoising methods. It also obtains competitive results
                 with the block-matching and 3D filtering method, which
                 is the current state-of-the-art for image denoising.",
  acknowledgement = ack-nhfb,
  articleno =    "2250033",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wagdy:2022:DCM,
  author =       "Marian Wagdy and Khaild Amin and Mina Ibrahim",
  title =        "Detection and Correction of Multi-Warping Document
                 Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500346",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500346",
  abstract =     "In this work, we aim to solve the multi-warping
                 document image problems. We can overcome the
                 limitations of the previous dewarping algorithms to
                 recover the shape of the document. The proposed method
                 is based on a well-defined pattern to simulate the
                 distorted and undistorted connected component of
                 document images. Some pairs of control points are
                 selected for each connected component and its ground
                 truth pattern to define the mapping function between
                 them. The dewarping process transforms the warping
                 connected component according to the geometric
                 transformation defined by the calculated mapping
                 function. Results on document dewarping dataset CBDAR
                 demonstrate the effectiveness of our method. OCR error
                 metrics are also used to evaluate the performance of
                 the proposed approach.",
  acknowledgement = ack-nhfb,
  articleno =    "2250034",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ghilas:2022:SDI,
  author =       "Hamza Ghilas and Meriem Gagaoua and Abdelkamel Tari
                 and Mohamed Cheriet",
  title =        "{Spatial Distribution of Ink at Keypoints (SDIK)}: a
                 Novel Feature for Word Spotting in {Arabic} Documents",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500358",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500358",
  abstract =     "This paper addresses the challenging task of word
                 spotting in Arabic handwritten documents. We proposed a
                 novel feature that we called Spatial Distribution of
                 Ink at Keypoints (SDIK). The proposed feature captures
                 the characteristics of Arabic handwriting concentrated
                 at endpoints and branch points. SDIK feature quantizes
                 the spatial repartition of ink pixels in the
                 neighborhoods of keypoints. The resulting SDIK features
                 are very fast to match, we take this advantage to match
                 a query word with lines images rather than words
                 images. By this matching mechanism, we overcome the
                 hard task of segmenting an Arabic document into words.
                 The method proposed in this study is tested on
                 historical Arabic document with IBN SINA dataset and on
                 modern handwriting with IFN/ENIT database. The obtained
                 results are great of interest for retrieving query
                 words in an Arabic document.",
  acknowledgement = ack-nhfb,
  articleno =    "2250035",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Salehi:2022:ISB,
  author =       "Hadi Salehi",
  title =        "Image De-Speckling Based on the Coefficient of
                 Variation, Improved Guided Filter, and Fast Bilateral
                 Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S021946782250036X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782250036X",
  abstract =     "Images are widely used in engineering. Unfortunately,
                 medical ultrasound images and synthetic aperture radar
                 (SAR) images are mainly degraded by an intrinsic noise
                 called speckle. Therefore, de-speckling is a main
                 pre-processing stage for degraded images. In this
                 paper, first, an optimized adaptive Wiener filter
                 (OAWF) is proposed. OAWF can be applied to the input
                 image without the need for logarithmic transform. In
                 addition its performance is improved. Next, the
                 coefficient of variation (CV) is computed from the
                 input image. With the help of CV, the guided filter
                 converts to an improved guided filter (IGF). Next, the
                 improved guided filter is applied on the image.
                 Subsequently, the fast bilateral filter is applied on
                 the image. The proposed filter has a better image
                 detail preservation compared to some other standard
                 methods. The experimental outcomes show that the
                 proposed denoising algorithm is able to preserve image
                 details and edges compared with other de-speckling
                 methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2250036",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hassan:2022:NSI,
  author =       "Gaber Hassan and Khalid M. Hosny and R. M. Farouk and
                 Ahmed M. Alzohairy",
  title =        "New Set of Invariant Quaternion {Krawtchouk} Moments
                 for Color Image Representation and Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500371",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500371",
  abstract =     "One of the most often used techniques to represent
                 color images is quaternion algebra. This study
                 introduces the quaternion Krawtchouk moments, QKrMs, as
                 a new set of moments to represent color images.
                 Krawtchouk moments (KrMs) represent one type of
                 discrete moments. QKrMs use traditional Krawtchouk
                 moments of each color channel to describe color images.
                 This new set of moments is defined by using orthogonal
                 polynomials called the Krawtchouk polynomials. The
                 stability against the translation, rotation, and
                 scaling transformations for QKrMs is discussed. The
                 performance of the proposed QKrMs is evaluated against
                 other discrete quaternion moments for image
                 reconstruction capability, toughness against various
                 types of noise, invariance to similarity
                 transformations, color face image recognition, and CPU
                 elapsed times.",
  acknowledgement = ack-nhfb,
  articleno =    "2250037",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Islam:2022:CSP,
  author =       "Rafiqul Islam and Md Shafiqul Islam and Muhammad
                 Shahin Uddin",
  title =        "Compressed Sensing in Parallel {MRI}: a Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500383",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500383",
  abstract =     "Magnetic resonance imaging (MRI) is a dynamic and safe
                 imaging technique in medical imaging. Recently,
                 parallel MRI (pMRI) is widely used for accelerating
                 conventional MRI. Both frequency and image domain-based
                 reconstructions are the most attractive methods for
                 generating the image from multi-channel k-space data.
                 Compressed sensing (CS) is a recently used procedure to
                 reduce the acquisition time of conventional MRI. This
                 reduction is achieved by taking fewer measurements from
                 the fully sampled k-space data. Therefore, applying the
                 CS technique in pMRI is the most emerging way for
                 further improving the acquisition time that is a
                 tremendous research interest. However, as the phase
                 encoding plane may be perpendicular or parallel to the
                 coil elements plane, finding the exact domain for CS in
                 pMRI reconstruction is a major challenging issue. In
                 this work, the application of the CS technique in pMRI
                 in both domains is investigated. Later some widely used
                 methodologies are presented as the nonlinear
                 reconstruction algorithm of CS in pMRI. Finally, a
                 discussion is performed based on CS in pMRI to perceive
                 the reality of different reconstruction algorithms at a
                 glance for finding preferred methodologies.",
  acknowledgement = ack-nhfb,
  articleno =    "2250038",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Arora:2022:CBP,
  author =       "Tanvi Arora",
  title =        "{CNN}-based Prediction of {COVID-19} using Chest {CT}
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500395",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500395",
  abstract =     "The coronavirus disease (COVID-19) pandemic that is
                 caused by the SARS-CoV2 has spread all over the world.
                 It is an infectious disease that can spread from person
                 to person. The severity of the disease can be
                 categorized into five categories namely asymptomatic,
                 mild, moderate, severe, and critical. From the reported
                 cases thus, it has been seen that 80\% of the cases
                 that test positive with COVID-19 infection have less
                 than moderate complications, whereas 20\% of the
                 positive cases develop severe and critical
                 complications. The virus infects the lungs of an
                 individual, therefore, it has been observed that the
                 X-ray and computed tomography (CT) scan images of the
                 infected people can be used by the machine
                 learning-based application programs to predict the
                 presence of the infection. Therefore, in the proposed
                 work, a Convolutional Neural Network model based upon
                 the DenseNet architecture is being used to predict the
                 presence of COVID-19 infection using the CT scan images
                 of the chest. The proposed work has been carried out
                 using the dataset of the CT images from the COVID CT
                 Dataset. It has 349 images marked as COVID-19 positive
                 and 397 images have been marked as COVID-19 negative.
                 The proposed system can categorize the test set images
                 with an accuracy of 91.4\%. The proposed method is
                 capable of detecting the presence of COVID-19 infection
                 with good accuracy using the chest CT scan images of
                 the humans.",
  acknowledgement = ack-nhfb,
  articleno =    "2250039",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Varghese:2022:DLB,
  author =       "Prathibha Varghese and G. Arockia Selva Saroja",
  title =        "Deep Learning-Based Hexrep Neural Network for
                 Convergence Free with Operator's Efficacy in Hexagonal
                 Image Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467823500328",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu Aug 11 08:52:44 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500328",
  abstract =     "The field of hexagonal image processing is concerned
                 with the creation of image processing systems that
                 combine the advantages of biological model-based
                 evolutionary motivated frameworks. The structure and
                 functionality of artificial neural networks were
                 inspired by biological processes. The fundamental
                 framework of recording and output devices limits their
                 present state of the art. Prior neural networks have
                 used square or hexagonal style input to completely
                 connected layers, which resulted in a high coherence
                 problem between two adjacent hexagonal kernel layers
                 due to pooling. Previous research does not design the
                 self-data structure to support convolution to increase
                 computational efficiency, so it violates the
                 convolution and pooling operator, which greatly
                 degrades the image process performance. This paper
                 introduces a novel paradigm Proficient Deep
                 Learning-based Hexrep Neural Network that overcomes
                 major significant problems in image operations
                 structure constraint, coherence problem, and violation
                 of convolution and pooling operator and achieves
                 hexagonal image processing.",
  acknowledgement = ack-nhfb,
  articleno =    "2350032",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kaur:2022:RND,
  author =       "Swapandeep Kaur and Sheifali Gupta and Swati Singh and
                 Tanvi Arora",
  title =        "A Review on Natural Disaster Detection in Social Media
                 and Satellite Imagery Using Machine Learning and Deep
                 Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467822500401",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500401",
  abstract =     "A disaster is a devastating incident that causes a
                 serious disruption of the functions of a community. It
                 leads to loss of human life and environmental and
                 financial losses. Natural disasters cause damage and
                 privation that could last for months and even years.
                 Immediate steps need to be taken and social media
                 platforms like Twitter help to provide relief to the
                 affected public. However, it is difficult to analyze
                 high-volume data obtained from social media posts.
                 Therefore, the efficiency and accuracy of useful data
                 extracted from the enormous posts related to disaster
                 are low. Satellite imagery is gaining popularity
                 because of its ability to cover large temporal and
                 spatial areas. But, both the social media and satellite
                 imagery require the use of automated methods to avoid
                 the errors caused by humans. Deep learning and machine
                 learning have become extremely popular for text and
                 image classification tasks. In this paper, a review has
                 been done on natural disaster detection through
                 information obtained from social media and satellite
                 images using deep learning and machine learning.",
  acknowledgement = ack-nhfb,
  articleno =    "2250040",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2022:DLB,
  author =       "V. Akash Kumar and Vijaya Mishra and Monika Arora",
  title =        "Deep Learning-Based Classification of Malignant and
                 Benign Cells in Dermatoscopic Images via Transfer
                 Learning Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500413",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500413",
  abstract =     "The inhibition of healthy cells creating improper
                 controlling process of the human body system indicates
                 the occurrence of growth of cancerous cells. The
                 cluster of such cells leads to the development of
                 tumor. The observation of this type of abnormal skin
                 pigmentation is done using an effective tool called
                 Dermoscopy. However, these dermatoscopic images possess
                 a great challenge for diagnosis. Considering the
                 characteristics of dermatoscopic images, transfer
                 learning is an appropriate approach of automatically
                 classifying the images based on the respective
                 categories. An automatic identification of skin cancer
                 not only saves human life but also helps in detecting
                 its growth at an earlier stage which saves medical
                 practitioner's effort and time. A newly predicted model
                 has been proposed for classifying the skin cancer as
                 benign or malignant by DCNN with transfer learning and
                 its pre-trained models such as VGG 16, VGG 19, ResNet
                 50, ResNet 101, and Inception V3. The proposed
                 methodology aims at examining the efficiency of
                 pre-trained models and transfer learning approach for
                 the classification tasks and opens new dimensions of
                 research in the field of medicines using imaging
                 technique which can be implementable in real-time
                 applications.",
  acknowledgement = ack-nhfb,
  articleno =    "2250041",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sasikumar:2022:CAD,
  author =       "K. Sasikumar and B. Vijayakumar",
  title =        "Comparative Analysis of Different Data Replication
                 Strategies in Cloud Environment",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500425",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib;
                 https://www.math.utah.edu/pub/tex/bib/java2020.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500425",
  abstract =     "In this paper, we performed a comparative study of the
                 different data replication strategies such as Adaptive
                 Data Replication Strategy (ADRS), Dynamic Cost Aware
                 Re-Replication and Rebalancing Strategy (DCR2S) and
                 Efficient Placement Algorithm (EPA) in the cloud
                 environment. The implementation of these three
                 techniques is done in JAVA and the performance analysis
                 is conducted to study the performance of those
                 replication techniques by various parameters. The
                 parameters used for the performance analysis of these
                 three techniques are Load Variance, Response Time,
                 Probability of File Availability, System Byte Effective
                 Rate (SBER), Latency, and Fault Ratio. From the
                 analysis, it is evaluated that by varying the number of
                 file replicas, it shows deviations in the outcomes of
                 these parameters. The comparative results were also
                 analyzed.",
  acknowledgement = ack-nhfb,
  articleno =    "2250042",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Padalkar:2022:FBS,
  author =       "Ganesh R. Padalkar and Madhuri B. Khambete",
  title =        "Fusion-Based Semantic Segmentation Using Deep Learning
                 Architecture in Case of Very Small Training Dataset",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500437",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500437",
  abstract =     "Semantic segmentation is a pre-processing step in
                 computer vision-based applications. It is the task of
                 assigning a predefined class label to every pixel of an
                 image. Several supervised and unsupervised algorithms
                 are available to classify pixels of an image into
                 predefined object classes. The algorithms, such as
                 random forest and SVM are used to obtain the semantic
                 segmentation. Recently, convolutional neural network
                 (CNN)-based architectures have become popular for the
                 tasks of object detection, object recognition, and
                 segmentation. These deep architectures perform semantic
                 segmentation with far better accuracy than the
                 algorithms that were used earlier. CNN-based deep
                 learning architectures require a large dataset for
                 training. In real life, some of the applications may
                 not have sufficient good quality samples for training
                 of deep learning architectures e.g. medical
                 applications. Such a requirement initiated a need to
                 have a technique of effective training of deep learning
                 architecture in case of a very small dataset. Class
                 imbalance is another challenge in the process of
                 training deep learning architecture. Due to class
                 imbalance, the classifier overclassifies classes with
                 large samples. In this paper, the challenge of training
                 a deep learning architecture with a small dataset and
                 class imbalance is addressed by novel fusion-based
                 semantic segmentation technique which improves
                 segmentation of minor and major classes.",
  acknowledgement = ack-nhfb,
  articleno =    "2250043",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Elyounsi:2022:FAO,
  author =       "Asma Elyounsi and Hatem Tlijani and Mohamed Salim
                 Bouhlel",
  title =        "Firefly Algorithm Optimized Functional Link Artificial
                 Neural Network for {ISA}-Radar Image Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500449",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500449",
  abstract =     "Traditional neural networks are very diverse and have
                 been used during the last decades in the fields of data
                 classification. These networks like MLP, back
                 propagation neural networks (BPNN) and feed forward
                 network have shown inability to scale with problem size
                 and with the slow convergence rate. So in order to
                 overcome these numbers of drawbacks, the use of higher
                 order neural networks (HONNs) becomes the solution by
                 adding input units along with a stronger functioning of
                 other neural units in the network and transforms easily
                 these input units to hidden layers. In this paper, a
                 new metaheuristic method, Firefly (FFA), is applied to
                 calculate the optimal weights of the Functional Link
                 Artificial Neural Network (FLANN) by using the flashing
                 behavior of fireflies in order to classify ISA-Radar
                 target. The average classification result of FLANN-FFA
                 which reached 96\% shows the efficiency of the process
                 compared to other tested methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2250044",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ravikumar:2022:MLT,
  author =       "S. Ravikumar and E. Kannan",
  title =        "Machine Learning Techniques for Identifying Fetal Risk
                 During Pregnancy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500450",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500450",
  abstract =     "Cardiotocography (CTG) is a biophysical method for
                 assessing fetal condition that primarily relies on the
                 recording and automated analysis of fetal heart
                 activity. The quantitative description of the CTG
                 signals is provided by computerized fetal monitoring
                 systems. Even though effective conclusion generation
                 methods for decision process support are still required
                 to find out the fetal risk such as premature embryo,
                 this proposed method and outcome data can confirm the
                 assessment of the fetal state after birth. Low birth
                 weight is quite possibly the main attribute that
                 significantly depicts an unusual fetal result. These
                 expectations are assessed in a constant experimental
                 decision support system, providing valuable information
                 that can be used to obtain additional information about
                 the fetal state using machine learning techniques. The
                 advancements in modern obstetric practice enabled the
                 use of numerous reliable and robust machine learning
                 approaches in classifying fetal heart rate signals. The
                 Na{\"\i}ve Bayes (NB) classifier, support vector
                 machine (SVM), decision trees (DT), and random forest
                 (RF) are used in the proposed method. To assess these
                 outcomes in the proposed method, some of the metrics
                 such as precision, accuracy, F1 score, recall,
                 sensitivity, logarithmic loss and mean absolute error
                 have been taken. The above mentioned metrics will be
                 helpful to predict the fetal risk.",
  acknowledgement = ack-nhfb,
  articleno =    "2250045",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bhasha:2022:AIS,
  author =       "A. Valli Bhasha and B. D. Venkatramana Reddy",
  title =        "Automated Image Super Resolution with the Aid of
                 Activation Function Optimized Deep {CNN} and Adaptive
                 Wavelet Lifting Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500462",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500462",
  abstract =     "Diverse image super-resolution (SR) techniques have
                 been implemented to reconstruct the high-resolution
                 (HR) images from input images through lower spatial
                 resolutions. However, the evaluation of the perceptual
                 quality of SR images remains an important and complex
                 research problem. This paper proposes a new image SR
                 model with the intention of attaining maximum Peak
                 Signal-to-Noise Ratio (PSNR). The conversion of
                 low-resolution (LR) images from the HR images is
                 performed by bicubic interpolation-based downsampling
                 and upsampling. Then, the four sub-bands of LR and HR
                 images are generated by the novel Adaptive Wavelet
                 Lifting approach, in which the filter modes are
                 optimized using the proposed SA-CBO. From this
                 technique, LR wavelet sub-bands (LRSB) for LR images
                 and HR wavelet sub-bands (HRSB) for HR images are
                 formed. With the help of the LRSB and HRSB images, the
                 residual images are formed by the adoption of the
                 optimized Activation function and optimized hidden
                 neurons in a deep convolutional neural network (CNN).
                 The improvement in both the adaptive wavelet lifting
                 approach and deep CNN is made by the
                 self-adaptive-colliding bodies optimization (SA-CBO).
                 Finally, the inverse adaptive wavelet lifting approach
                 is used to produce the final SR image. Experimental
                 results on publicly available SR image quality
                 databases confirm the effectiveness and generalization
                 ability of the proposed method compared with the
                 traditional image quality assessment algorithms.",
  acknowledgement = ack-nhfb,
  articleno =    "2250046",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2022:ICB,
  author =       "Gangavarapu Venkata Satya Kumar and P. G. Krishna
                 Mohan",
  title =        "Improved Content Based Image Retrieval Process Based
                 on Deep Convolutional Neural Network and Salp Swarm
                 Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500474",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500474",
  abstract =     "Digital image and medical image retrieval from several
                 repositories are improving gradually, so the capacity
                 of repositories increases rapidly. The semantic space
                 is the main issue on content-based image retrieval
                 (CBIR), which exists among the semantic level as well
                 as increases the data recognized through human and low
                 level visible data obtained through the image. The CBIR
                 system utilizes the deep convolutional neural network
                 (DCNN), which is trained to medical image
                 characterization and the digital image by salp swarm
                 optimization algorithm (SSA). The average
                 classification accuracy for medical image is 86.805\%,
                 a mean average precision is 79\%, Average Recall Rate
                 (ARR) is 91.7\% and F -measure is 84.9\%, are achieved
                 during retrieval task. For image retrieval, the Average
                 Precision Rate (APR) improved from 39\%, 40\%, 36\% and
                 42.5\% to 86.8\% and the ARR enhanced from 39.5\%,
                 40.5\%, 35.5\% and 42.5\% to 86.8\%. The F -measure is
                 improved from 39.5\%, 40.5\%, 35.5\% and 42.5\% to
                 86.8\% as different with Local tetra patterns (LTrP),
                 LOOP, local derivative pattern (LDP) and local mean
                 differential excitation pattern (LMDeP) separately on
                 Corel-1K dataset. The presented method is most suitable
                 for multimodal digital images and medical image
                 retrieval for various parts of the body.",
  acknowledgement = ack-nhfb,
  articleno =    "2250047",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shahrokhi:2022:ICM,
  author =       "Marziye Shahrokhi and Alireza Akoushideh and Asadollah
                 Shahbahrami",
  title =        "Image Copy--Move Forgery Detection Using Combination
                 of Scale-Invariant Feature Transform and Local Binary
                 Pattern Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500486",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500486",
  abstract =     "Today, manipulating, storing, and sending digital
                 images are simple and easy because of the development
                 of digital imaging devices from hardware and software
                 points of view. Digital images are used in different
                 contexts of people's lives such as news, forensics, and
                 so on. Therefore, the reliability of received images is
                 a question that often occupies the viewer's mind and
                 the authenticity of digital images is increasingly
                 important. Detecting a forged image as a genuine one as
                 well as detecting a genuine image as a forged one can
                 sometimes have irreparable consequences. For example,
                 an image that is available from the scene of a crime
                 can lead to a wrong decision if it is detected
                 incorrectly. In this paper, we propose a combination
                 method to improve the accuracy of copy--move forgery
                 detection (CMFD) reducing the false positive rate (FPR)
                 based on texture attributes. The proposed method uses a
                 combination of the scale-invariant feature transform
                 (SIFT) and local binary pattern (LBP). Consideration of
                 texture features around the keypoints detected by the
                 SIFT algorithm can be effective to reduce the incorrect
                 matches and improve the accuracy of CMFD. In addition,
                 to find more and better keypoints some pre-processing
                 methods have been proposed. This study was evaluated on
                 the COVERAGE, GRIP, and MICC-F220 databases.
                 Experimental results show that the proposed method
                 without clustering or segmentation and only with simple
                 matching operations, has been able to earn the true
                 positive rates of 98.75\%, 95.45\%, and 87\% on the
                 GRIP, MICC-F220, and COVERAGE datasets, respectively.
                 Also, the proposed method, with FPRs from 17.75\% to
                 3.75\% on the GRIP dataset, has been able to achieve
                 the best results.",
  acknowledgement = ack-nhfb,
  articleno =    "2250048",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Saha:2022:TBF,
  author =       "Priya Saha and Debotosh Bhattacharjee and Barin Kumar
                 De and Mita Nasipuri",
  title =        "A Thermal Blended Facial Expression Analysis and
                 Recognition System Using Deformed Thermal Facial
                 Areas",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500498",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500498",
  abstract =     "There are many research works in visible as well as
                 thermal facial expression analysis and recognition.
                 Several facial expression databases have been designed
                 in both modalities. However, little attention has been
                 given for analyzing blended facial expressions in the
                 thermal infrared spectrum. In this paper, we have
                 introduced a Visual-Thermal Blended Facial Expression
                 Database (VTBE) that contains visual and thermal face
                 images with both basic and blended facial expressions.
                 The database contains 12 posed blended facial
                 expressions and spontaneous six basic facial
                 expressions in both modalities. In this paper, we have
                 proposed Deformed Thermal Facial Area (DTFA) in thermal
                 expressive face image and make an analysis to
                 differentiate between basic and blended expressions
                 using DTFA. Here, the fusion of DTFA and Deformed
                 Visual Facial Area (DVFA) has been proposed combining
                 the features of both modalities and experiments and has
                 been conducted on this new database. However, to show
                 the effectiveness of our proposed approach, we have
                 compared our method with state-of-the-art methods using
                 USTC-NVIE database. Experiment results reveal that our
                 approach is superior to state-of-the-art methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2250049",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jadav:2022:DSD,
  author =       "Kalpesh R. Jadav and Arvind R. Yadav",
  title =        "Dynamic Shadow Detection and Removal for Vehicle
                 Tracking System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500504",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500504",
  abstract =     "Shadow leads to failure of moving target positioning,
                 segmentation, tracking, and classification in the video
                 surveillance system thus shadow detection and removal
                 is essential for further computer vision process. The
                 existing state-of-the-art methods for dynamic shadow
                 detection have produced a high discrimination rate but
                 a poor detection rate (foreground pixels are classified
                 as shadow pixels). This paper proposes an effective
                 method for dynamic shadow detection and removal based
                 on intensity ratio along with frame difference, gamma
                 correction, and morphology operations. The performance
                 of the proposed method has been tested on two outdoor
                 ATON datasets, namely, highway-I and highway-III for
                 vehicle tracking systems. The proposed method has
                 produced a discrimination rate of 89.07\% and a
                 detection rate of 80.79\% for highway-I video
                 sequences. Similarly, for a highway-III video sequence,
                 the discrimination rate of 85.60\% and detection rate
                 of 84.05\% have been obtained. Investigational outcomes
                 show that the proposed method is the simple, steadiest,
                 and robust for dynamic shadow detection on the dataset
                 used in this work.",
  acknowledgement = ack-nhfb,
  articleno =    "2250050",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Indhumathi:2022:HAR,
  author =       "C. Indhumathi and V. Murugan and G. Muthulakshmii",
  title =        "Human Action Recognition Using Spatio-Temporal
                 Multiplier Network and Attentive Correlated Temporal
                 Feature",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500516",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500516",
  abstract =     "Nowadays, action recognition has gained more attention
                 from the computer vision community. Normally for
                 recognizing human actions, spatial and temporal
                 features are extracted. Two-stream convolutional neural
                 network is used commonly for human action recognition
                 in videos. In this paper, Adaptive motion Attentive
                 Correlated Temporal Feature (ACTF) is used for temporal
                 feature extractor. The temporal average pooling in
                 inter-frame is used for extracting the inter-frame
                 regional correlation feature and mean feature. This
                 proposed method has better accuracy of 96.9\% for
                 UCF101 and 74.6\% for HMDB51 datasets, respectively,
                 which are higher than the other state-of-the-art
                 methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2250051",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jatain:2022:EFR,
  author =       "Rashmi Jatain and Manisha Jailia",
  title =        "Enhanced Face Recognition Using Adaptive Local Tri
                 {Weber} Pattern with Improved Deep Learning
                 Architecture",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "22",
  number =       "05",
  pages =        "??--??",
  month =        oct,
  year =         "2022",
  DOI =          "https://doi.org/10.1142/S0219467822500528",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Tue Nov 8 11:46:54 MST 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822500528",
  abstract =     "Effective face recognition is accomplished using the
                 extraction of features and classification. Though there
                 are multiple techniques for face image recognition,
                 full face recognition in real-time is quite difficult.
                 One of the emerging and promising methods to address
                 this challenge in face recognition is deep learning
                 networks. The inevitable network tool associated with
                 the face recognition method with deep learning systems
                 is convolutional neural networks (CNNs). This research
                 intends to develop a new method for face recognition
                 using adaptive intelligent methods. The main phases of
                 the proposed method are (a) data collection, (b) image
                 pre-processing, (c) normalization, (d) pattern
                 extraction, and (e) recognition. Initially, the images
                 for face recognition are gathered from CPFW, Yale
                 datasets, and the MIT-CBCL dataset. The image
                 pre-processing is performed by the Gaussian filtering
                 method. Further, the normalization of the image will be
                 done, which is a process that alters the range of pixel
                 intensities and can handle the poor contrast due to
                 glare. Then a new descriptor called adaptive local tri
                 Weber pattern (ALTrWP) acts as a pattern extractor. In
                 the recognition phase, the VGG16 architecture with new
                 chick updated-chicken swarm optimization (NSU-CSO) is
                 used. As the modification, VGG16 architecture will be
                 enhanced by this optimization technique. The
                 performance of the developed method is analyzed on two
                 standards face database. Experimental results are
                 compared with different machine learning approaches
                 concerned with noteworthy measures, which demonstrate
                 the efficiency of the considered classifier.",
  acknowledgement = ack-nhfb,
  articleno =    "2250052",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Raj:2023:DRV,
  author =       "R. Jisha Raj and Smitha Dharan and T. T. Sunil",
  title =        "Dimensionality Reduction and Visualization of {{\em
                 Bharatanatyam Mudras}}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467823500018",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500018",
  abstract =     "Cultural dances are practiced all over the world. The
                 study of various gestures of the performer using
                 computer vision techniques can help in better
                 understanding of these dance forms and for annotation
                 purposes. {\em Bharatanatyam\/} is a classical dance
                 that originated in South India. {\em Bharatanatyam\/}
                 performer uses hand gestures ( {\em mudras\/} ), facial
                 expressions and body movements to communicate to the
                 audience the intended meaning. According to {\em
                 Natyashastra}, a classical text on Indian dance, there
                 are 28 {\em Asamyukta Hastas\/} (single-hand gestures)
                 and 23 {\em Samyukta Hastas\/} (Double-hand gestures)
                 in {\em Bharatanatyam}. Open datasets on {\em
                 Bharatanatyam\/} dance gestures are not presently
                 available. An exhaustive open dataset comprising of
                 various {\em mudras\/} in {\em Bharatanatyam\/} was
                 created. The dataset consists of 15\,396 distinct
                 single-hand {\em mudra\/} images and 13\,035 distinct
                 double-hand {\em mudra\/} images. In this paper, we
                 explore the dataset using various multidimensional
                 visualization techniques. PCA, Kernel PCA, Local Linear
                 Embedding, Multidimensional Scaling, Isomap, t-SNE and
                 PCA--t-SNE combination are being investigated. The best
                 visualization for exploration of the dataset is
                 obtained using PCA--t-SNE combination.",
  acknowledgement = ack-nhfb,
  articleno =    "2350001",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Adhikari:2023:PEC,
  author =       "Ramesh Adhikari and Suresh Pokharel",
  title =        "Performance Evaluation of Convolutional Neural Network
                 Using Synthetic Medical Data Augmentation Generated by
                 {GAN}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S021946782350002X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782350002X",
  abstract =     "Data augmentation is widely used in image processing
                 and pattern recognition problems in order to increase
                 the richness in diversity of available data. It is
                 commonly used to improve the classification accuracy of
                 images when the available datasets are limited. Deep
                 learning approaches have demonstrated an immense
                 breakthrough in medical diagnostics over the last
                 decade. A significant amount of datasets are needed for
                 the effective training of deep neural networks. The
                 appropriate use of data augmentation techniques
                 prevents the model from over-fitting and thus increases
                 the generalization capability of the network while
                 testing afterward on unseen data. However, it remains a
                 huge challenge to obtain such a large dataset from rare
                 diseases in the medical field. This study presents the
                 synthetic data augmentation technique using Generative
                 Adversarial Networks to evaluate the generalization
                 capability of neural networks using existing data more
                 effectively. In this research, the convolutional neural
                 network (CNN) model is used to classify the X-ray
                 images of the human chest in both normal and pneumonia
                 conditions; then, the synthetic images of the X-ray
                 from the available dataset are generated by using the
                 deep convolutional generative adversarial network
                 (DCGAN) model. Finally, the CNN model is trained again
                 with the original dataset and augmented data generated
                 using the DCGAN model. The classification performance
                 of the CNN model is improved by 3.2\% when the
                 augmented data were used along with the originally
                 available dataset.",
  acknowledgement = ack-nhfb,
  articleno =    "2350002",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumawat:2023:IDH,
  author =       "Anchal Kumawat and Sucheta Panda",
  title =        "An Integrated Double Hybrid Fusion Approach for Image
                 Smoothing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500031",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500031",
  abstract =     "Often in practice, during the process of image
                 acquisition, the acquired image gets degraded due to
                 various factors like noise, motion blur, mis-focus of a
                 camera, atmospheric turbulence, etc. resulting in the
                 image unsuitable for further analysis or processing. To
                 improve the quality of these degraded images, a double
                 hybrid restoration filter is proposed on the two same
                 sets of input images and the output images are fused to
                 get a unified filter in combination with the concept of
                 image fusion. First image set is processed by applying
                 deconvolution using Wiener Filter (DWF) twice and
                 decomposing the output image using Discrete Wavelet
                 Transform (DWT). Similarly, second image set is also
                 processed simultaneously by applying Deconvolution
                 using Lucy--Richardson Filter (DLR) twice followed by
                 the above procedure. The proposed filter gives a better
                 performance as compared to DWF and DLR filters in case
                 of both blurry as well as noisy images. The proposed
                 filter is compared with some standard deconvolution
                 algorithms and also some state-of-the-art restoration
                 filters with the help of seven image quality assessment
                 parameters. Simulation results prove the success of the
                 proposed algorithm and at the same time, visual and
                 quantitative results are very impressive.",
  acknowledgement = ack-nhfb,
  articleno =    "2350003",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Antony:2023:TFE,
  author =       "Joycy K. Antony and K. Kanagalakshmi",
  title =        "{T2FRF} Filter: an Effective Algorithm for the
                 Restoration of Fingerprint Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500043",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500043",
  abstract =     "Images captured in dim light are hardly satisfactory
                 and increasing the International Organization for
                 Standardization (ISO) for a short duration of exposure
                 makes them noisy. The image restoration methods have a
                 wide range of applications in the field of medical
                 imaging, computer vision, remote sensing, and graphic
                 design. Although the use of flash improves the
                 lighting, it changed the image tone besides developing
                 unnecessary highlight and shadow. Thus, these drawbacks
                 are overcome using the image restoration methods that
                 recovered the image with high quality from the degraded
                 observation. The main challenge in the image
                 restoration approach is recovering the degraded image
                 contaminated with the noise. In this research, an
                 effective algorithm, named T2FRF filter, is developed
                 for the restoration of the image. The noisy pixel is
                 identified from the input fingerprint image using Deep
                 Convolutional Neural Network (Deep CNN), which is
                 trained using the neighboring pixels. The Rider
                 Optimization Algorithm (ROA) is used for the removal of
                 the noisy pixel in the image. The enhancement of the
                 pixel is performed using the type II fuzzy system. The
                 developed T2FRF filter is measured using the metrics,
                 such as correlation coefficient and Peak Signal to
                 Noise Ratio (PSNR) for evaluating the performance. When
                 compared with the existing image restoration method,
                 the developed method obtained a maximum correlation
                 coefficient of 0.7504 and a maximum PSNR of 28.2467dB,
                 respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2350004",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sharma:2023:PAS,
  author =       "Sandhya Sharma and Sheifali Gupta and Neeraj Kumar and
                 Tanvi Arora",
  title =        "Postal Automation System in {Gurmukhi} Script using
                 Deep Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500055",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500055",
  abstract =     "Nowadays in the era of automation, the postal
                 automation system is one of the major research areas.
                 Developing a postal automation system for a nation like
                 India is much troublesome than other nations because of
                 India's multi-script and multi-lingual behavior. This
                 proposed work will be helpful in the postal automation
                 of district names of Punjab (state) written in Gurmukhi
                 script, which is the official language of the state in
                 North India. For this, a holistic approach i.e. a
                 segmentation-free technique has been used with the help
                 of Convolutional Neural Network (CNN) and Deep learning
                 (DL). For the purpose of recognition, a database of 22
                 000 images (samples) which are handwritten in Gurmukhi
                 script for all the 22 districts of Punjab is prepared.
                 Each sample is written two times by 500 different
                 writers generating 1000 samples for each district name.
                 Two CNN models are proposed which are named as
                 ConvNetGuru and ConvNetGuruMod for the purpose of
                 recognition. Maximum validation accuracy achieved by
                 ConvNetGuru is 90\% and ConvNetGuruMod is 98\%.",
  acknowledgement = ack-nhfb,
  articleno =    "2350005",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Erwin:2023:RBV,
  author =       "Erwin and Hadrians Kesuma Putra and Bambang Suprihatin
                 and Fathoni",
  title =        "Retinal Blood Vessel Extraction Using a New
                 Enhancement Technique of Modified Convolution Filters
                 and {Sauvola} Thresholding",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500067",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500067",
  abstract =     "The retinal blood vessels in humans are major
                 components with different shapes and sizes. The
                 extraction of the blood vessels from the retina is an
                 important step to identify the type or nature of the
                 pattern of the diseases in the retina. Furthermore, the
                 retinal blood vessel was also used for diagnosis,
                 detection, and classification. The most recent solution
                 in this topic is to enable retinal image improvement or
                 enhancement by a convolution filter and Sauvola
                 threshold. In image enhancement, gamma correction is
                 applied before filtering the retinal fundus. After
                 that, the image should be transformed to a gray channel
                 to enhance pictorial clarity using contrast-limited
                 histogram equalization. For filter, this paper combines
                 two convolution filters, namely sharpen and smooth
                 filters. The Sauvola threshold, the morphology, and the
                 medium filter are applied to extract blood vessels from
                 the retinal image. This paper uses DRIVE and STARE
                 datasets. The accuracies of the proposed method are
                 95.37\% for DRIVE with a runtime of 1.77s and 95.17\%
                 for STARE with 2.05s runtime. Based on the result, it
                 concludes that the proposed method is good enough to
                 achieve average calculation parameters of a low time
                 quality, quick, and significant.",
  acknowledgement = ack-nhfb,
  articleno =    "2350006",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Tudavekar:2023:STI,
  author =       "Gajanan Tudavekar and Santosh S. Saraf and Sanjay R.
                 Patil",
  title =        "Spatio-Temporal Inference Transformer Network for
                 Video Inpainting",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500079",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500079",
  abstract =     "Video inpainting aims to complete in a visually
                 pleasing way the missing regions in video frames. Video
                 inpainting is an exciting task due to the variety of
                 motions across different frames. The existing methods
                 usually use attention models to inpaint videos by
                 seeking the damaged content from other frames.
                 Nevertheless, these methods suffer due to irregular
                 attention weight from spatio-temporal dimensions, thus
                 giving rise to artifacts in the inpainted video. To
                 overcome the above problem, Spatio-Temporal Inference
                 Transformer Network (STITN) has been proposed. The
                 STITN aligns the frames to be inpainted and
                 concurrently inpaints all the frames, and a
                 spatio-temporal adversarial loss function improves the
                 STITN. Our method performs considerably better than the
                 existing deep learning approaches in quantitative and
                 qualitative evaluation.",
  acknowledgement = ack-nhfb,
  articleno =    "2350007",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Devi:2023:DSF,
  author =       "Bhagyashri Devi and M. Mary Synthuja Jain Preetha",
  title =        "A Descriptive Survey on Face Emotion Recognition
                 Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500080",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500080",
  abstract =     "Recognition of natural emotion from human faces has
                 applications in Human--Computer Interaction, image and
                 video retrieval, automated tutoring systems, smart
                 environment as well as driver warning systems. It is
                 also a significant indication of nonverbal
                 communication among the individuals. The assignment of
                 Face Emotion Recognition (FER) is predominantly complex
                 for two reasons. The first reason is the nonexistence
                 of a large database of training images, and the second
                 one is about classifying the emotions, which can be
                 complex based on the static input image. In addition,
                 robust unbiased FER in real time remains the foremost
                 challenge for various supervised learning-based
                 techniques. This survey analyzes diverse techniques
                 regarding the FER systems. It reviews a bunch of
                 research papers and performs a significant analysis.
                 Initially, the analysis depicts various techniques that
                 are contributed in different research papers. In
                 addition, this paper offers a comprehensive study
                 regarding the chronological review and performance
                 achievements in each contribution. The analytical
                 review is also concerned about the measures for which
                 the maximum performance was achieved in several
                 contributions. Finally, the survey is extended with
                 various research issues and gaps that can be useful for
                 the researchers to promote improved future works on the
                 FER models.",
  acknowledgement = ack-nhfb,
  articleno =    "2350008",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Canejo:2023:EDN,
  author =       "Marcos Jos{\'e} Can{\^e}jo and Carlos Alexandre
                 {Barros De Mello}",
  title =        "Edge Detection in Natural Scenes Inspired by the Speed
                 Drawing Challenge",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500092",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500092",
  abstract =     "Edge detection is a major step in several computer
                 vision applications. Edges define the shape of objects
                 to be used in a recognition system, for example. In
                 this work, we introduce an approach to edge detection
                 inspired by a challenge for artists: the Speed Drawing
                 Challenge. In this challenge, a person is asked to draw
                 the same figure in different times (as 10 min, 1 min
                 and 10 s); at each time, different levels of details
                 are drawn by the artist. In a short time stamp, just
                 the major elements remain. This work proposes a new
                 approach for producing images with different amounts of
                 edges representing different levels of relevance. Our
                 method uses superpixel to suppress image details,
                 followed by Globalized Probability of Boundary (gPb)
                 and Canny edge detection algorithms to create an image
                 containing different number of edges. After that, an
                 edge analysis step detects whose edges are the most
                 relevant for the scene. The results are presented for
                 the BSDS500 dataset and they are compared to other edge
                 and contour detection algorithms by quantitative and
                 qualitative means with very satisfactory results.",
  acknowledgement = ack-nhfb,
  articleno =    "2350009",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gaddour:2023:NMA,
  author =       "Houda Gaddour and Slim Kanoun and Nicole Vincent",
  title =        "A New Method for {Arabic} Text Detection in Natural
                 Scene Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500109",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500109",
  abstract =     "Text in scene images can provide useful and vital
                 information for content-based image analysis.
                 Therefore, text detection and script identification in
                 images are an important task. In this paper, we propose
                 a new method for text detection in natural scene
                 images, particularly for Arabic text, based on a
                 bottom-up approach where four principal steps can be
                 highlighted. The detection of extremely stable and
                 homogeneous regions of interest (ROIs) is based on the
                 Color Stability and Homogeneity Regions (CSHR) proposed
                 technique. These regions are then labeled as textual or
                 non-textual ROI. This identification is based on a
                 structural approach. The textual ROIs are grouped to
                 constitute zones according to spatial relations between
                 them. Finally, the textual or non-textual nature of the
                 constituted zones is refined. This last identification
                 is based on handcrafted features and on features built
                 from a Convolutional Neural Network (CNN) after
                 learning. The proposed method was evaluated on the
                 databases used for text detection in natural scene
                 images: the competitions organized in 2017 edition of
                 the International Conference on Document Analysis and
                 Recognition (ICDAR2017), the Urdu-text database and our
                 Natural Scene Image Database for Arabic Text detection
                 (NSIDAT) database. The obtained experimental results
                 seem to be interesting.",
  acknowledgement = ack-nhfb,
  articleno =    "2350010",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lepcha:2023:IMC,
  author =       "Dawa Chyophel Lepcha and Bhawna Goyal and Ayush
                 Dogra",
  title =        "Image Matting: a Comprehensive Survey on Techniques,
                 Comparative Analysis, Applications and Future Scope",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500110",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500110",
  abstract =     "In the era of rapid growth of technologies, image
                 matting plays a key role in image and video editing
                 along with image composition. In many significant
                 real-world applications such as film production, it has
                 been widely used for visual effects, virtual zoom,
                 image translation, image editing and video editing.
                 With recent advancements in digital cameras, both
                 professionals and consumers have become increasingly
                 involved in matting techniques to facilitate image
                 editing activities. Image matting plays an important
                 role to estimate {\em alpha matte\/} in the {\em
                 unknown\/} region to distinguish {\em foreground\/}
                 from the {\em background\/} region of an image using an
                 input image and the corresponding trimap of an image
                 which represents a {\em foreground\/} and {\em
                 unknown\/} region. Numerous image matting techniques
                 have been proposed recently to extract high-quality
                 {\em matte\/} from image and video sequences. This
                 paper illustrates a systematic overview of the current
                 image and video matting techniques mostly emphasis on
                 the current and advanced algorithms proposed recently.
                 In general, image matting techniques have been
                 categorized according to their underlying approaches,
                 namely, sampling-based, propagation-based, combination
                 of sampling and propagation-based and deep
                 learning-based algorithms. The traditional image
                 matting algorithms depend primarily on color
                 information to predict {\em alpha matte\/} such as
                 sampling-based, propagation-based or combination of
                 sampling and propagation-based algorithms. However,
                 these techniques mostly use low-level features and
                 suffer from high-level {\em background\/} which tends
                 to produce unwanted artifacts when color is same or
                 semi-transparent in the {\em foreground\/} object.
                 Image matting techniques based on deep learning have
                 recently introduced to address the shortcomings of
                 traditional algorithms. Rather than simply depending on
                 the color information, it uses deep learning mechanism
                 to estimate the {\em alpha matte\/} using an input
                 image and the trimap of an image. A comprehensive
                 survey on recent image matting algorithms and in-depth
                 comparative analysis of these algorithms has been
                 thoroughly discussed in this paper.",
  acknowledgement = ack-nhfb,
  articleno =    "2350011",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Elmoufidi:2023:CMI,
  author =       "Abdelali Elmoufidi and Ayoub Skouta and Said
                 Jai-andaloussi and Ouail Ouchetto",
  title =        "{CNN} with Multiple Inputs for Automatic Glaucoma
                 Assessment Using Fundus Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500122",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:33 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500122",
  abstract =     "In the area of ophthalmology, glaucoma affects an
                 increasing number of people. It is a major cause of
                 blindness. Early detection avoids severe ocular
                 complications such as glaucoma, cystoid macular edema,
                 or diabetic proliferative retinopathy. Intelligent
                 artificial intelligence has been confirmed beneficial
                 for glaucoma assessment. In this paper, we describe an
                 approach to automate glaucoma diagnosis using funds
                 images. The setup of the proposed framework is in
                 order: The Bi-dimensional Empirical Mode Decomposition
                 (BEMD) algorithm is applied to decompose the Regions of
                 Interest (ROI) to components (BIMFs+residue). CNN
                 architecture VGG19 is implemented to extract features
                 from decomposed BEMD components. Then, we fuse the
                 features of the same ROI in a bag of features. These
                 last very long; therefore, Principal Component Analysis
                 (PCA) are used to reduce features dimensions. The bags
                 of features obtained are the input parameters of the
                 implemented classifier based on the Support Vector
                 Machine (SVM). To train the built models, we have used
                 two public datasets, which are ACRIMA and REFUGE. For
                 testing our models, we have used a part of ACRIMA and
                 REFUGE plus four other public datasets, which are
                 RIM-ONE, ORIGA-light, Drishti-GS1, and sjchoi86-HRF.
                 The overall precision of 98.31\%, 98.61\%, 96.43\%,
                 96.67\%, 95.24\%, and 98.60\% is obtained on ACRIMA,
                 REFUGE, RIM-ONE, ORIGA-light, Drishti-GS1, and
                 sjchoi86-HRF datasets, respectively, by using the model
                 trained on REFUGE. Again an accuracy of 98.92\%,
                 99.06\%, 98.27\%, 97.10\%, 96.97\%, and 96.36\% is
                 obtained in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light,
                 Drishti-GS1, and sjchoi86-HRF datasets, respectively,
                 using the model training on ACRIMA. The experimental
                 results obtained from different datasets demonstrate
                 the efficiency and robustness of the proposed approach.
                 A comparison with some recent previous work in the
                 literature has shown a significant advancement in our
                 proposal.",
  acknowledgement = ack-nhfb,
  articleno =    "2350012",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sangani:2023:PSS,
  author =       "Dhara J. Sangani and Rajesh A. Thakker and S. D.
                 Panchal and Rajesh Gogineni",
  title =        "{Pan}-Sharpening for Spectral Details Preservation Via
                 Convolutional Sparse Coding in Non-Subsampled Shearlet
                 Space",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467823500134",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500134",
  abstract =     "The optical satellite sensors encounter certain
                 constraints on producing high-resolution multispectral
                 (HRMS) images. Pan-sharpening (PS) is a remote sensing
                 image fusion technique, which is an effective mechanism
                 to overcome the limitations of available imaging
                 products. The prevalent issue in PS algorithms is the
                 imbalance between spatial quality and spectral details
                 preservation, thereby producing intensity variations in
                 the fused image. In this paper, a PS method is proposed
                 based on convolutional sparse coding (CSC) implemented
                 in the non-subsampled shearlet transform (NSST) domain.
                 The source images, panchromatic (PAN) and multispectral
                 (MS) images, are decomposed using NSST. The resultant
                 high-frequency bands are fused using adaptive weights
                 determined from chaotic grey wolf optimization (CGWO)
                 algorithm. The CSC-based model is employed to fuse the
                 low-frequency bands. Further, an iterative filtering
                 mechanism is developed to enhance the quality of fused
                 image. Four datasets with different geographical
                 content like urban area, vegetation, etc. and eight
                 existing algorithms are used for evaluation of the
                 proposed PS method. The comprehensive visual and
                 quantitative results approve that the proposed method
                 accomplishes considerable improvement in spatial and
                 spectral details equivalence in the pan-sharpened
                 image.",
  acknowledgement = ack-nhfb,
  articleno =    "2350013",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fathima:2023:MIC,
  author =       "M. Dhilsath Fathima and R. Hariharan and S. P. Raja",
  title =        "Multiple Imputation by Chained Equations ---
                 {$K$}-Nearest Neighbors and Deep Neural Network
                 Architecture for Kidney Disease Prediction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500146",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500146",
  abstract =     "Chronic kidney disease (CKD) is a health concern that
                 affects people all over the world. Kidney dysfunction
                 or impaired kidney functions are the causes of CKD. The
                 machine learning-based prediction models are used to
                 determine the risk level of CKD and assist healthcare
                 practitioners in delaying and preventing the disease's
                 progression. The researchers proposed many prediction
                 models for determining the CKD risk level. Although
                 these models performed well, their precision is limited
                 since they do not handle missing values in the clinical
                 dataset adequately. The missing values of a clinical
                 dataset can degrade the training outcomes that leads to
                 false predictions. Thus, imputing missing values
                 increases the prediction model performance. This
                 proposed work developed a novel imputation technique by
                 combining Multiple Imputation by Chained Equations and
                 K -Nearest Neighbors (MICE--KNN) for imputing the
                 missing values. The experimental results show that
                 MICE--KNN accurately predicts the missing values, and
                 the Deep Neural Network (DNN) improves the prediction
                 performance of the CKD model. Various metrics like mean
                 absolute error, accuracy, specificity, Matthews
                 correlation coefficient, the area under the curve, $
                 F_1$-score, sensitivity, and precision have been used
                 to evaluate the proposed CKD model performance. The
                 performance analysis exhibits that MICE--KNN with deep
                 learning outperforms other classifiers. According to
                 our experimental study, the MICE--KNN imputation
                 algorithm with DNN is more appropriate for predicting
                 the kidney disease.",
  acknowledgement = ack-nhfb,
  articleno =    "2350014",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jahnavi:2023:NES,
  author =       "Yeturu Jahnavi and Poongothai Elango and S. P. Raja
                 and P. Nagendra Kumar",
  title =        "A Novel Ensemble Stacking Classification of Genetic
                 Variations Using Machine Learning Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500158",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500158",
  abstract =     "Genetics is the clinical review of congenital
                 mutation, where the principal advantage of analyzing
                 genetic mutation of humans is the exploration,
                 analysis, interpretation and description of the genetic
                 transmitted and inherited effect of several diseases
                 such as cancer, diabetes and heart diseases. Cancer is
                 the most troublesome and disordered affliction as the
                 proportion of cancer sufferers is growing massively.
                 Identification and discrimination of the mutations that
                 impart to the enlargement of tumor from the unbiased
                 mutations is difficult, as majority tumors of cancer
                 are able to exercise genetic mutations. The genetic
                 mutations are systematized and categorized to sort the
                 cancer by way of medical observations and considering
                 clinical studies. At the present time, genetic
                 mutations are being annotated and these interpretations
                 are being accomplished either manually or using the
                 existing primary algorithms. Evaluation and
                 classification of each and every individual genetic
                 mutation was basically predicated on evidence from
                 documented content built on medical literature.
                 Consequently, as a means to build genetic mutations,
                 basically, depending on the clinical evidences persists
                 a challenging task. There exist various algorithms such
                 as one hot encoding technique is used to derive
                 features from genes and their variations, TF-IDF is
                 used to extract features from the clinical text data.
                 In order to increase the accuracy of the
                 classification, machine learning algorithms such as
                 support vector machine, logistic regression, Naive
                 Bayes, etc., are experimented. A stacking model
                 classifier has been developed to increase the accuracy.
                 The proposed stacking model classifier has obtained the
                 log loss 0.8436 and 0.8572 for cross-validation data
                 set and test data set, respectively. By the
                 experimentation, it has been proved that the proposed
                 stacking model classifier outperforms the existing
                 algorithms in terms of log loss. Basically, minimum log
                 loss refers to the efficient model. Here the log loss
                 has been reduced to less than 1 by using the proposed
                 stacking model classifier. The performance of these
                 algorithms can be gauged on the basis of the various
                 measures like multi-class log loss.",
  acknowledgement = ack-nhfb,
  articleno =    "2350015",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shashikala:2023:SSM,
  author =       "T. D. Shashikala and B. L. Sunitha and S.
                 Basavarajappa and J. P. Davim",
  title =        "Some Studies on Measurement of Worn Surface by Digital
                 Image Processing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S021946782350016X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782350016X",
  abstract =     "Digital image processing (DIP) becomes a common tool
                 for analyzing engineering problems by fast, frequent
                 and noncontact method of identification and
                 measurement. An attempt has been made in the present
                 investigation to use this method for automatically
                 detecting the worn regions on the material surface and
                 also its measurement. Brass material has been used for
                 experimentation as it is used generally as a bearing
                 material. A pin on disc dry sliding wear testing
                 machine has been used for conducting the experiments by
                 applying loads from 10 N to 50 N and by keeping sliding
                 distance and sliding speed constant. After testing,
                 images are acquired by using 1/2 inch interline
                 transfer CCD image sensor with 795(H) {\^a} 896(V)
                 spatial resolution of 8.6 {\textmu} m (H) {\^a} 8.3
                 {\textmu} m (V) unit cell. Denoising has been done to
                 remove any possible noise followed by contrast
                 stretching to enhance image for wear region extraction.
                 Segmentation tool was used to divide the worn and
                 unworn regions by identifying white regions greater
                 than a threshold value with an objective of quantifying
                 the worn surface for tested specimen. Canny edge
                 detection and granulometry techniques have been used to
                 quantify the wear region. The results revel that the
                 specific wear rate increases with increase in applied
                 load, at constant sliding speed and sliding distance.
                 Similarly, the area of worn region as identified by DIP
                 also increased from 42.7\% to 69.97\%. This is because
                 of formation of deeper groves in the worn material.",
  acknowledgement = ack-nhfb,
  articleno =    "2350016",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Spoorthi:2023:FCS,
  author =       "B. Spoorthi and Shanthi Mahesh",
  title =        "Firefly Competitive Swarm Optimization Based
                 Hierarchical Attention Network for Lung Cancer
                 Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500171",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500171",
  abstract =     "Lung cancer is a severe disease, which causes high
                 deaths in the world. Earlier discovery of lung cancer
                 is useful to enhance the rate of survival in patients.
                 Computed Tomography (CT) is utilized for determining
                 the tumor and identifying the cancer level in the body.
                 However, the issues of CT images cause less tumor
                 visibility areas and unconstructive rates in tumor
                 regions. This paper devises an optimization-driven
                 technique for classifying lung cancer. The CT image is
                 utilized for determining the position of the tumor.
                 Here, the CT image undergoes segmentation, which is
                 performed using the DeepJoint model. Furthermore, the
                 feature extraction is carried out, wherein features
                 such as local ternary pattern-based features, Histogram
                 of Gradients (HoG) features, and statistical features,
                 like variance, mean, kurtosis, energy, entropy, and
                 skewness. The categorization of lung cancer is
                 performed using Hierarchical Attention Network (HAN).
                 The training of HAN is carried out using proposed
                 Firefly Competitive Swarm Optimization (FCSO), which is
                 devised by combining firefly algorithm (FA), and
                 Competitive Swarm Optimization (CSO). The proposed
                 FCSO-based HAN provided effective performance with high
                 accuracy of 91.3\%, sensitivity of 88\%, and
                 specificity of 89.1\%.",
  acknowledgement = ack-nhfb,
  articleno =    "2350017",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mondal:2023:DCW,
  author =       "Saorabh Kumar Mondal and Arpitam Chatterjee and Bipan
                 Tudu",
  title =        "{DCT} Coefficients Weighting ({DCTCW})-Based {Gray
                 Wolf Optimization (GWO)} for Brightness Preserving
                 Image Contrast Enhancement",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500183",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500183",
  abstract =     "Image contrast enhancement (CE) is a frequent image
                 enhancement requirement in diverse applications.
                 Histogram equalization (HE), in its conventional and
                 different further improved ways, is a popular technique
                 to enhance the image contrast. The conventional as well
                 as many of the later versions of HE algorithms often
                 cause loss of original image characteristics
                 particularly brightness distribution of original image
                 that results artificial appearance and feature loss in
                 the enhanced image. Discrete Cosine Transform (DCT)
                 coefficient mapping is one of the recent methods to
                 minimize such problems while enhancing the image
                 contrast. Tuning of DCT parameters plays a crucial role
                 towards avoiding the saturations of pixel values.
                 Optimization can be a possible solution to address this
                 problem and generate contrast enhanced image preserving
                 the desired original image characteristics. Biological
                 behavior-inspired optimization techniques have shown
                 remarkable betterment over conventional optimization
                 techniques in different complex engineering problems.
                 Gray wolf optimization (GWO) is a comparatively new
                 algorithm in this domain that has shown promising
                 potential. The objective function has been formulated
                 using different parameters to retain original image
                 characteristics. The objective evaluation against CEF,
                 PCQI, FSIM, BRISQUE and NIQE with test images from
                 three standard databases, namely, SIPI, TID and CSIQ
                 shows that the presented method can result in values up
                 to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the
                 stated metrics which are competitive to the reported
                 conventional and improved techniques. This paper can be
                 considered a first-time application of GWO towards
                 DCT-based image CE.",
  acknowledgement = ack-nhfb,
  articleno =    "2350018",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lohith:2023:MBP,
  author =       "M. S. Lohith and Yoga Suhas Kuruba Manjunath and M. N.
                 Eshwarappa",
  title =        "Multimodal Biometric Person Authentication Using Face,
                 Ear and Periocular Region Based on Convolution Neural
                 Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500195",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500195",
  abstract =     "Biometrics is an active area of research because of
                 the increase in need for accurate person identification
                 in numerous applications ranging from entertainment to
                 security. Unimodal and multimodal are the well-known
                 biometric methods. Unimodal biometrics uses one
                 biometric modality of a person for person
                 identification. The performance of an unimodal
                 biometric system is degraded due to certain limitations
                 such as: intra-class variations and nonuniversality.
                 The person identification using more than one biometric
                 modality of a person is multimodal biometrics. This
                 method of identification has gained more interest due
                 to resistance on spoof attacks and more recognition
                 rate. Conventional methods of feature extraction have
                 difficulty in engineering features that are liable to
                 more variations such as illumination, pose and age
                 variations. Feature extraction using convolution neural
                 network (CNN) can overcome these difficulties because
                 large dataset with robust variations can be used for
                 training, where CNN can learn these variations. In this
                 paper, we propose multimodal biometrics at feature
                 level horizontal fusion using face, ear and periocular
                 region biometric modalities and apply deep learning CNN
                 for feature representation and also we propose face,
                 ear and periocular region dataset that are robust to
                 intra-class variations. The evaluation of the system is
                 made by using proposed database. Accuracy, Precision,
                 Recall and F1 score are calculated to evaluate the
                 performance of the system and had shown remarkable
                 improvement over existing biometric system.",
  acknowledgement = ack-nhfb,
  articleno =    "2350019",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sudha:2023:CES,
  author =       "K. Antony Sudha and V. Cibi Castro and G. Muthulakshmi
                 and T. Ilam Parithi and S. P. Raja",
  title =        "A Chaotic Encryption System Based on {DNA} Coding
                 Using a Deep Neural Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500201",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500201",
  abstract =     "Critical to computer vision applications, deep
                 learning demands a massive volume of training data for
                 great performance. However, encrypting the sensitive
                 information in a photograph is fraught with difficulty,
                 despite rapid technological advancements. The Advanced
                 Encryption System (AES) is the bedrock of classical
                 encryption technologies. The Data Encryption Standard
                 (DES) has low sensitivity, with weak anti-hacking
                 capabilities. In a chaotic encryption system, a chaotic
                 logistic map is employed to generate a key double
                 logistic sequence, and deoxyribonucleic acid (DNA)
                 matrices are created by DNA coding. The XOR operation
                 is carried out between the DNA sequence matrix and the
                 key matrix. Finally, the DNA matrix is decoded to
                 obtain an encrypted image. Given that encrypted images
                 are susceptible to attacks, a rapid and efficient
                 Convolutional Neural Network (CNN) denoiser is used
                 that enhances the robustness of the algorithm by
                 maximizing the resolution of rebuilt images. The use of
                 a key mixing percentage factor gives the proposed
                 system vast key space and great key sensitivity. Its
                 implementation is examined using statistical techniques
                 such as histogram analysis, information entropy, key
                 space analysis and key sensitivity. Experiments have
                 shown that the suggested system is secure and robust to
                 statistical and noise attacks.",
  acknowledgement = ack-nhfb,
  articleno =    "2350020",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Das:2023:NSE,
  author =       "Gyanesh Das and Rutuparna Panda and Leena Samantaray
                 and Sanjay Agrawal",
  title =        "A Novel Segmentation Error Minimization-Based Method
                 for Multilevel Optimal Threshold Selection Using
                 Opposition Equilibrium Optimizer",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500213",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500213",
  abstract =     "Image segmentation is imperative for image processing
                 applications. Thresholding technique is the easiest way
                 of partitioning an image into different regions.
                 Mostly, entropy-based threshold selection methods are
                 used for multilevel thresholding. However, these
                 methods suffer from their dependencies on spatial
                 distribution of gray values. To solve this issue, a
                 novel segmentation error minimization (SEM)-based
                 method for multilevel optimal threshold selection using
                 opposition equilibrium optimizer (OEO) is suggested. In
                 this contribution, a new segmentation score (SS)
                 (objective function) is derived while minimizing the
                 segmentation error function. Our proposal is explicitly
                 free from gray level spatial distribution of an image.
                 Optimal threshold values are achieved by maximizing the
                 SS (fitness value) using OEO. The key to success is the
                 maximization of score among classes, ensuring the
                 sharpening of the shred boundary between classes,
                 leading to an improved threshold selection method. It
                 is empirically demonstrated how the optimal threshold
                 selection is made. Experimental results are presented
                 using standard test images. Standard measures like
                 PSNR, SSIM and FSIM are used for validation The results
                 are compared with state-of-the-art entropy-based
                 technique. Our method performs well both qualitatively
                 and quantitatively. The suggested technique would be
                 useful for biomedical image segmentation.",
  acknowledgement = ack-nhfb,
  articleno =    "2350021",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pemula:2023:GRF,
  author =       "Rambabu Pemula and Sagenela Vijaya Kumar and C.
                 Nagaraju",
  title =        "Generation of Random Fields for Image Segmentation
                 Techniques: a Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500225",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500225",
  abstract =     "Generation of random fields (GRF) for image
                 segmentation represents partitioning an image into
                 different regions that are homogeneous or have similar
                 facets of the image. It is one of the most challenging
                 tasks in image processing and a very important
                 pre-processing step in the fields of computer vision,
                 image analysis, medical image processing, pattern
                 recognition, remote sensing, and geographical
                 information system. Many researchers have presented
                 numerous image segmentation approaches, but still,
                 there are challenges like segmentation of low contrast
                 images, removal of shadow in the images, reduction of
                 high dimensional images, and computational complexity
                 of segmentation techniques. In this review paper, the
                 authors address these issues. The experiments are
                 conducted and tested on the Berkely dataset (BSD500),
                 Semantic dataset, and our own dataset, and the results
                 are shown in the form of tables and graphs.",
  acknowledgement = ack-nhfb,
  articleno =    "2350022",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Raju:2023:PAC,
  author =       "Ayalapogu Ratna Raju and Suresh Pabboju and Rajeswara
                 Rao Ramisetty",
  title =        "Performance Analysis and Critical Review on
                 Segmentation Techniques for Brain Tumor
                 Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500237",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500237",
  abstract =     "An irregular growth in brain cells causes brain
                 tumors. In recent years, a considerable rate of
                 increment in medical cases regarding brain tumors has
                 been observed, affecting adults and children. However,
                 it is highly curable in recent times only if detected
                 in the early time of tumor growth. Moreover, there are
                 many sophisticated approaches devised by researchers
                 for predicting the tumor regions and their stages. In
                 addition, Magnetic Resonance Imaging (MRI) is utilized
                 commonly by radiologists to evaluate tumors. In this
                 paper, the input image is from a database, and brain
                 tumor segmentation is performed using various
                 segmentation techniques. Here, the comparative analysis
                 is performed by comparing the performance of
                 segmentation approaches, like Hybrid Active Contour
                 (HAC) model, Bayesian Fuzzy Clustering (BFC), Active
                 Contour (AC), Fuzzy C-Means (FCM) clustering technique,
                 Sparse (Sparse FCM), and Black Hole Entropy Fuzzy
                 Clustering (BHEFC) model. Moreover, segmentation
                 technique performance is evaluated with the Dice
                 coefficient, Jaccard coefficient, and segmentation
                 accuracy. The proposed method shows high Dice and
                 Jaccard coefficients of 0.7809 and 0.6456 by varying
                 iteration with the REMBRANDT dataset and a better
                 segmentation accuracy of 0.9789 by changing image size
                 in the Brats-2015 database.",
  acknowledgement = ack-nhfb,
  articleno =    "2350023",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chamundeshwari:2023:HPE,
  author =       "Chamundeshwari and Nagashetteppa Biradar and
                 Udaykumar",
  title =        "Hybrid Pattern Extraction with Deep Learning-Based
                 Heart Disease Diagnosis Using Echocardiogram Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500249",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Mar 25 07:40:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500249",
  abstract =     "Echocardiography represents a noninvasive diagnostic
                 approach that offers information concerning
                 hemodynamics and cardiac function. It is a familiar
                 cardiovascular diagnostic test apart from chest X-ray
                 and echocardiography. The medical knowledge is enhanced
                 by the Artificial Intelligence (AI) approaches like
                 deep learning and machine learning because of the
                 increase in the complexity as well as the volume of the
                 data that in turn unlocks the clinically significant
                 information. Similarly, the usage of developing
                 information as well as communication technologies is
                 becoming important for generating a persistent
                 healthcare service via which the chronic disease and
                 elderly patients get their medical facility at their
                 home that in turn enhances the life quality and avoids
                 hospitalizations. The main intention of this paper is
                 to design and develop a novel heart disease diagnosis
                 using speckle-noise reduction and deep learning-based
                 feature learning and classification. The datasets
                 gathered from the hospital are composed of both the
                 images and the video frames. Since echocardiogram
                 images suffer from speckle noise, the initial process
                 is the speckle-noise reduction technique. Then, the
                 pattern extraction is performed by combining the Local
                 Binary Pattern (LBP), and Weber Local Descriptor (WLD)
                 referred to as the hybrid pattern extraction. The deep
                 feature learning is conducted by the optimized
                 Convolutional Neural Network (CNN), in which the
                 features are extracted from the max-pooling layer, and
                 the fully connected layer is replaced by the optimized
                 Recurrent Neural Network (RNN) for handling the
                 diagnosis of heart disease, thus proposed model is
                 termed as CRNN. The novel Adaptive Electric Fish
                 Optimization (A-EFO) is used for performing feature
                 learning and classification. In the final step, the
                 best accuracy is achieved with the introduced model,
                 while a comparative analysis is accomplished over the
                 traditional models. From the experimental analysis, FDR
                 of A-EFO-CRNN at 75\% learning percentage is 21.05\%,
                 15\%, 48.89\%, and 71.95\% progressed than CRNN, CNN,
                 RNN, and NN, respectively. Thus, the performance of the
                 A-EFO-CRNN is enriched than the existing
                 heuristic-oriented and classifiers in terms of the
                 image dataset.",
  acknowledgement = ack-nhfb,
  articleno =    "2350024",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Vasantharaj:2023:ABI,
  author =       "A. Vasantharaj and Pacha Shoba Rani and Sirajul Huque
                 and K. S. Raghuram and R. Ganeshkumar and Sebahadin
                 Nasir Shafi",
  title =        "Automated Brain Imaging Diagnosis and Classification
                 Model using Rat Swarm Optimization with Deep Learning
                 based Capsule Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467822400010",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400010",
  abstract =     "Earlier identification of brain tumor (BT) is
                 essential to increase the survival rate of the
                 patients. The commonly used imaging technique for BT
                 diagnosis is magnetic resonance imaging (MRI).
                 Automated BT classification model is required for
                 assisting the radiologists to save time and enhance
                 efficiency. The classification of BT is difficult owing
                 to the non-uniform shapes of tumors and location of
                 tumors in the brain. Therefore, deep learning (DL)
                 models can be employed for the effective
                 identification, prediction, and diagnosis of diseases.
                 In this view, this paper presents an automated BT
                 diagnosis using rat swarm optimization (RSO) with deep
                 learning based capsule network (DLCN) model, named
                 RSO-DLCN model. The presented RSO-DLCN model involves
                 bilateral filtering (BF) based preprocessing to enhance
                 the quality of the MRI. Besides, non-iterative grabcut
                 based segmentation (NIGCS) technique is applied to
                 detect the affected tumor regions. In addition, DLCN
                 model based feature extractor with RSO algorithm based
                 parameter optimization processes takes place. Finally,
                 extreme learning machine with stacked autoencoder
                 (ELM-SA) based classifier is employed for the effective
                 classification of BT. For validating the BT diagnostic
                 performance of the presented RSO-DLCN model, an
                 extensive set of simulations were carried out and the
                 results are inspected under diverse dimensions. The
                 simulation outcome demonstrated the promising results
                 of the RSO-DLCN model on BT diagnosis with the
                 sensitivity of 98.4\%, specificity of 99\%, and
                 accuracy of 98.7\%.",
  acknowledgement = ack-nhfb,
  articleno =    "2240001",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Kiran:2023:MLD,
  author =       "S. Vishwa Kiran and Inderjeet Kaur and K. Thangaraj
                 and V. Saveetha and R. Kingsy Grace and N. Arulkumar",
  title =        "Machine Learning with Data Science-Enabled Lung Cancer
                 Diagnosis and Classification Using Computed Tomography
                 Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400022",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400022",
  abstract =     "In recent times, the healthcare industry has been
                 generating a significant amount of data in distinct
                 formats, such as electronic health records (EHR),
                 clinical trials, genetic data, payments, scientific
                 articles, wearables, and care management databases.
                 Data science is useful for analysis (pattern
                 recognition, hypothesis testing, risk valuation) and
                 prediction. The major, primary usage of data science in
                 the healthcare domain is in medical imaging. At the
                 same time, lung cancer diagnosis has become a hot
                 research topic, as automated disease detection poses
                 numerous benefits. Although numerous approaches have
                 existed in the literature for lung cancer diagnosis,
                 the design of a novel model to automatically identify
                 lung cancer is a challenging task. In this view, this
                 paper designs an automated machine learning (ML) with
                 data science-enabled lung cancer diagnosis and
                 classification (MLDS-LCDC) using computed tomography
                 (CT) images. The presented model initially employs
                 Gaussian filtering (GF)-based pre-processing technique
                 on the CT images collected from the lung cancer
                 database. Besides, they are fed into the normalized
                 cuts (Ncuts) technique where the nodule in the
                 pre-processed image can be determined. Moreover, the
                 oriented FAST and rotated BRIEF (ORB) technique is
                 applied as a feature extractor. At last, sunflower
                 optimization-based wavelet neural network (SFO-WNN)
                 model is employed for the classification of lung
                 cancer. In order to examine the diagnostic outcome of
                 the MLDS-LCDC model, a set of experiments were carried
                 out and the results are investigated in terms of
                 different aspects. The resultant values demonstrated
                 the effectiveness of the MLDS-LCDC model over the other
                 state-of-the-art methods with the maximum sensitivity
                 of 97.01\%, specificity of 98.64\%, and accuracy of
                 98.11\%.",
  acknowledgement = ack-nhfb,
  articleno =    "2240002",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Sammeta:2023:DOS,
  author =       "Naresh Sammeta and Latha Parthiban",
  title =        "Data Ownership and Secure Medical Data Transmission
                 using Optimal Multiple Key-Based Homomorphic Encryption
                 with Hyperledger Blockchain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400034",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400034",
  abstract =     "Recent healthcare systems are defined as highly
                 complex and expensive. But it can be decreased with
                 enhanced electronic health records (EHR) management,
                 using blockchain technology. The healthcare sector in
                 today's world needs to address two major issues, namely
                 data ownership and data security. Therefore, blockchain
                 technology is employed to access and distribute the
                 EHRs. With this motivation, this paper presents novel
                 data ownership and secure medical data transmission
                 model using optimal multiple key-based homomorphic
                 encryption (MHE) with Hyperledger blockchain
                 (OMHE-HBC). The presented OMHE-HBC model enables the
                 patients to access their own data, provide permission
                 to hospital authorities, revoke permission from
                 hospital authorities, and permit emergency contacts.
                 The proposed model involves the MHE technique to
                 securely transmit the data to the cloud and prevent
                 unauthorized access to it. Besides, the optimal key
                 generation process in the MHE technique takes place
                 using a hosted cuckoo optimization (HCO) algorithm. In
                 addition, the proposed model enables sharing of EHRs by
                 the use of multi-channel HBC, which makes use of one
                 blockchain to save patient visits and another one for
                 the medical institutions in recoding links that point
                 to EHRs stored in external systems. A complete set of
                 experiments were carried out in order to validate the
                 performance of the suggested model, and the results
                 were analyzed under many aspects. A comprehensive
                 comparison of results analysis reveals that the
                 suggested model outperforms the other techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "2240003",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Madhusudhan:2023:FVR,
  author =       "M. V. Madhusudhan and V. Udaya Rani and Chetana
                 Hegde",
  title =        "Finger Vein Recognition Model for Biometric
                 Authentication Using Intelligent Deep Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400046",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400046",
  abstract =     "In recent years, biometric authentication systems have
                 remained a hot research topic, as they can recognize or
                 authenticate a person by comparing their data to other
                 biometric data stored in a database. Fingerprints, palm
                 prints, hand vein, finger vein, palm vein, and other
                 anatomic or behavioral features have all been used to
                 develop a variety of biometric approaches. Finger vein
                 recognition (FVR) is a common method of examining the
                 patterns of the finger veins for proper authentication
                 among the various biometrics. Finger vein acquisition,
                 preprocessing, feature extraction, and authentication
                 are all part of the proposed intelligent deep
                 learning-based FVR (IDL-FVR) model. Infrared imaging
                 devices have primarily captured the use of finger
                 veins. Furthermore, a region of interest extraction
                 process is carried out in order to save the finger
                 part. The shark smell optimization algorithm is used to
                 tune the hyperparameters of the bidirectional
                 long--short-term memory model properly. Finally, an
                 authentication process based on Euclidean distance is
                 performed, which compares the features of the current
                 finger vein image to those in the database. The IDL-FVR
                 model surpassed the earlier methods by accomplishing a
                 maximum accuracy of 99.93\%. Authentication is
                 successful when the Euclidean distance is small and
                 vice versa.",
  acknowledgement = ack-nhfb,
  articleno =    "2240004",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Banerjee:2023:SVD,
  author =       "Rudranath Banerjee and Sourav De and Shouvik Dey",
  title =        "A Survey on Various Deep Learning Algorithms for an
                 Efficient Facial Expression Recognition System",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400058",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400058",
  abstract =     "Facial Expression (FE) encompasses information
                 concerning the emotional together with the physical
                 state of a human. In the last few years, FE Recognition
                 (FER) has turned out to be a propitious research field.
                 FER is the chief processing technique for non-verbal
                 intentions, and also it is a significant and propitious
                 computer vision together with the artificial
                 intelligence field. As a novel machine learning theory,
                 Deep Learning (DL) not only highlights the depth of the
                 learning model but also emphasizes the significance of
                 Feature Learning (FL) for the network model, and it has
                 made several research achievements in FER. Here, the
                 present research states are examined typically from the
                 latest FE extraction algorithm as well as the FER
                 centered on DL. The research on classifiers gathered
                 from recent papers discloses a more powerful as well as
                 reliable comprehending of the peculiar traits of
                 classifiers for research fellows. At the ending of the
                 survey, few problems in addition to chances that are
                 required to be tackled in the upcoming future are
                 presented.",
  acknowledgement = ack-nhfb,
  articleno =    "2240005",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Thushara:2023:GTB,
  author =       "A. Thushara and C. Ushadevi Amma and Ansamma John",
  title =        "Graph Theory-Based Brain Network Connectivity Analysis
                 and Classification of {Alzheimer}'s Disease",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S021946782240006X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782240006X",
  abstract =     "Alzheimer's Disease (AD) is basically a progressive
                 neurodegenerative disorder associated with abnormal
                 brain networks that affect millions of elderly people
                 and degrades their quality of life. The abnormalities
                 in brain networks are due to the disruption of White
                 Matter (WM) fiber tracts that connect the brain
                 regions. Diffusion-Weighted Imaging (DWI) captures the
                 brain's WM integrity. Here, the correlation betwixt the
                 WM degeneration and also AD is investigated by
                 utilizing graph theory as well as Machine Learning (ML)
                 algorithms. By using the DW image obtained from
                 Alzheimer's Disease Neuroimaging Initiative (ADNI)
                 database, the brain graph of each subject is
                 constructed. The features extracted from the brain
                 graph form the basis to differentiate between Mild
                 Cognitive Impairment (MCI), Control Normal (CN) and AD
                 subjects. Performance evaluation is done using binary
                 and multiclass classification algorithms and obtained
                 an accuracy that outperforms the current top-notch
                 DWI-based studies.",
  acknowledgement = ack-nhfb,
  articleno =    "2240006",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Malathi:2023:RRC,
  author =       "V. Malathi and M. P. Gopinath",
  title =        "A Review on Rice Crop Disease Classification Using
                 Computational Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400071",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400071",
  abstract =     "Rice is a significant cereal crop across the world. In
                 rice cultivation, different types of sowing methods are
                 followed, and thus bring in issues regarding sampling
                 collection. Climate, soil, water level, and a
                 diversified variety of crop seeds (hybrid and
                 traditional varieties) and the period of growth are
                 some of the challenges. This survey mainly focuses on
                 rice crop diseases which affect the parts namely
                 leaves, stems, roots, and spikelet; it mainly focuses
                 on leaf-based diseases. Existing methods for diagnosing
                 leaf disease include statistical approaches, data
                 mining, image processing, machine learning, and deep
                 learning techniques. This review mainly addresses
                 diseases of the rice crop, a framework to diagnose rice
                 crop diseases, and computational approaches in Image
                 Processing, Machine Learning, Deep Learning, and
                 Convolutional Neural Networks. Based on performance
                 indicators, interpretations were made for the following
                 algorithms namely support vector machine (SVM),
                 convolutional neural network (CNN), backpropagational
                 neural network (BPNN), and feedforward neural network
                 (FFNN).",
  acknowledgement = ack-nhfb,
  articleno =    "2240007",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Kumar:2023:HBV,
  author =       "T. Satish Kumar and S. Jothilakshmi and Batholomew C.
                 James and M. Prakash and N. Arulkumar and C. Rekha",
  title =        "{HHO}-Based Vector Quantization Technique for
                 Biomedical Image Compression in Cloud Computing",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400083",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/datacompression.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400083",
  abstract =     "In the present digital era, the exploitation of
                 medical technologies and massive generation of medical
                 data using different imaging modalities, adequate
                 storage, management, and transmission of biomedical
                 images necessitate image compression techniques. Vector
                 quantization (VQ) is an effective image compression
                 approach, and the widely employed VQ technique is
                 Linde--Buzo--Gray (LBG), which generates local optimum
                 codebooks for image compression. The codebook
                 construction is treated as an optimization issue solved
                 with utilization of metaheuristic optimization
                 techniques. In this view, this paper designs an
                 effective biomedical image compression technique in the
                 cloud computing (CC) environment using Harris Hawks
                 Optimization (HHO)-based LBG techniques. The HHO-LBG
                 algorithm achieves a smooth transition among
                 exploration as well as exploitation. To investigate the
                 better performance of the HHO-LBG technique, an
                 extensive set of simulations was carried out on
                 benchmark biomedical images. The proposed HHO-LBG
                 technique has accomplished promising results in terms
                 of compression performance and reconstructed image
                 quality.",
  acknowledgement = ack-nhfb,
  articleno =    "2240008",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Kumar:2023:WTO,
  author =       "K. Praveen Kumar and C. Venkata Narasimhulu and K.
                 Satya Prasad",
  title =        "{$2$D} Wavelet Tree Ordering Based Localized Total
                 Variation Model for Efficient Image Restoration",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400095",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400095",
  abstract =     "The degraded image during the process of image
                 analysis needs more number of iterations to restore it.
                 These iterations take long waiting time and slow
                 scanning, resulting in inefficient image restoration. A
                 few numbers of measurements are enough to recuperate an
                 image with good condition. Due to tree sparsity, a 2D
                 wavelet tree reduces the number of coefficients and
                 iterations to restore the degraded image. All the
                 wavelet coefficients are extracted with overlaps as low
                 and high sub-band space and ordered them such that they
                 are decomposed in the tree ordering structured path.
                 Some articles have addressed the problems with tree
                 sparsity and total variation (TV), but few authors
                 endorsed the benefits of tree sparsity. In this paper,
                 a spatial variation regularization algorithm based on
                 tree order is implemented to change the window size and
                 variation estimators to reduce the loss of image
                 information and to solve the problem of image smoothing
                 operation. The acceptance rate of the tree-structured
                 path relies on local variation estimators to regularize
                 the performance parameters and update them to restore
                 the image. For this, the Localized Total Variation
                 (LTV) method is proposed and implemented on a 2D
                 wavelet tree ordering structured path based on the
                 proposed image smooth adjustment scheme. In the end, a
                 reliable reordering algorithm proposed to reorder the
                 set of pixels and to increase the reliability of the
                 restored image. Simulation results clearly show that
                 the proposed method improved the performance compared
                 to existing methods of image restoration.",
  acknowledgement = ack-nhfb,
  articleno =    "2240009",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Reddy:2023:MMM,
  author =       "Mummadi Gowthami Reddy and Palagiri Veera Narayana
                 Reddy and Patil Ramana Reddy",
  title =        "Multi-Modal Medical Image Fusion Using 3-Stage
                 Multiscale Decomposition and {PCNN} with Adaptive
                 Arguments",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400101",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400101",
  abstract =     "In the current era of technological development,
                 medical imaging plays an important role in many
                 applications of medical diagnosis and therapy. In this
                 regard, medical image fusion could be a powerful tool
                 to combine multi-modal images by using image processing
                 techniques. But, conventional approaches failed to
                 provide the effective image quality assessments and
                 robustness of fused image. To overcome these drawbacks,
                 in this work three-stage multiscale decomposition
                 (TSMSD) using pulse-coupled neural networks with
                 adaptive arguments (PCNN-AA) approach is proposed for
                 multi-modal medical image fusion. Initially,
                 nonsubsampled shearlet transform (NSST) is applied onto
                 the source images to decompose them into low frequency
                 and high frequency bands. Then, low frequency bands of
                 both the source images are fused using nonlinear
                 anisotropic filtering with discrete Karhunen--Loeve
                 transform (NLAF-DKLT) methodology. Next, high frequency
                 bands obtained from NSST are fused using PCNN-AA
                 approach. Now, fused low frequency and high frequency
                 bands are reconstructed using NSST reconstruction.
                 Finally, band fusion rule algorithm with pyramid
                 reconstruction is applied to get final fused medical
                 image. Extensive simulation outcome discloses the
                 superiority of proposed TSMSD using PCNN-AA approach as
                 compared to state-of-the-art medical image fusion
                 methods in terms of fusion quality metrics such as
                 entropy (E), mutual information (MI), mean (M),
                 standard deviation (STD), correlation coefficient (CC)
                 and computational complexity.",
  acknowledgement = ack-nhfb,
  articleno =    "2240010",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Naveen:2023:FDA,
  author =       "J. Naveen and Sheba Selvam and Blessy Selvam",
  title =        "{FO-DPSO} Algorithm for Segmentation and Detection of
                 Diabetic Mellitus for Ulcers",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400113",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400113",
  abstract =     "In recent days, the major concern for diabetic
                 patients is foot ulcers. According to the survey, among
                 15 people among 100 are suffering from this foot ulcer.
                 The wound or ulcer found which is found in diabetic
                 patients consumes more time to heal, also required more
                 conscious treatment. Foot ulcers may lead to
                 deleterious danger condition and also may be the cause
                 for loss of limb. By understanding this grim condition,
                 this paper proposes Fractional-Order Darwinian Particle
                 Swarm Optimization (FO-DPSO) technique for analyzing
                 foot ulcer 2D color images. This paper deals with
                 standard image processing, i.e. efficient segmentation
                 using FO-DPSO algorithm and extracting textural
                 features using Gray Level Co-occurrence Matrix (GLCM)
                 technique. The whole effort projected results as
                 accuracy of 91.2\%, sensitivity of 100\% and
                 specificity as 96.7\% for Na{\"\i}ve Bayes classifier
                 and accuracy of 91.2\%, sensitivity of 100\% and
                 sensitivity of 79.6\% for Hoeffding tree classifier.",
  acknowledgement = ack-nhfb,
  articleno =    "2240011",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Gayatri:2023:CID,
  author =       "Erapaneni Gayatri and S. L. Aarthy",
  title =        "Challenges and Imperatives of Deep Learning Approaches
                 for Detection of Melanoma: a Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400125",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400125",
  abstract =     "Recently, melanoma became one of the deadliest forms
                 of skin cancer due to ultraviolet rays. The diagnosis
                 of melanoma is very crucial if it is not identified in
                 the early stages and later on, in the advanced stages,
                 it affects the other organs of the body, too. Earlier
                 identification of melanoma plays a major role in the
                 survival chances of a human. The manual detection of
                 tumor thickness is a very difficult task so dermoscopy
                 is used to measure the thickness of the tumor which is
                 a non-invasive method. Computer-aided diagnosis is one
                 of the greatest evolutions in the medical sector, this
                 system helps the doctors for the automated diagnosis of
                 the disease because it improves accurate disease
                 detection. In the world of digital images, some phases
                 are required to remove the artifacts for achieving the
                 best accurate diagnosis results such as the acquisition
                 of an image, pre-processing, segmentation, feature
                 selection, extraction and finally classification phase.
                 This paper mainly focuses on the various deep learning
                 techniques like convolutional neural networks,
                 recurrent neural networks, You Only Look Once for the
                 purpose of classification and prediction of the
                 melanoma and is also focuses on the other variant of
                 melanomas, i.e. ocular melanoma and mucosal melanoma
                 because it is not a matter where the melanoma starts in
                 the body.",
  acknowledgement = ack-nhfb,
  articleno =    "2240012",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Rao:2023:DLB,
  author =       "E. Srinivasa Rao and Ch. Raghava Prasad",
  title =        "Deep Learning-Based Medical Image Fusion Using
                 Integrated Joint Slope Analysis with Probabilistic
                 Parametric Steered Image Filter",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467822400137",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Jun 2 06:51:21 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467822400137",
  abstract =     "Medical image fusion plays a significant role in
                 medical diagnosis applications. Although the
                 conventional approaches have produced moderate visual
                 analysis, still there is a scope to improve the
                 performance parameters and reduce the computational
                 complexity. Thus, this article implemented the hybrid
                 fusion method by using the novel implementation of
                 joint slope analysis (JSA), probabilistic parametric
                 steered image filtration (PPSIF), and deep learning
                 convolutional neural networks (DLCNNs)-based SR Fusion
                 Net. Here, JSA decomposes the images to estimate
                 edge-based slopes and develops the edge-preserving
                 approximate layers from the multi-modal medical images.
                 Further, PPSIF is used to generate the feature fusion
                 with base layer-based weight maps. Then, the SR Fusion
                 Net is used to generate the spatial and texture
                 feature-based weight maps. Finally, optimal fusion rule
                 is applied on the detail layers generated from the base
                 layer and approximate layer, which resulted in the
                 fused outcome. The proposed method is capable of
                 performing the fusion operation between various
                 modalities of images, such as MRI-CT, MRI-PET, and
                 MRI-SPECT combinations by using two different
                 architectures. The simulation results show that the
                 proposed method resulted in better subjective and
                 objective performance as compared to state of art
                 approaches.",
  acknowledgement = ack-nhfb,
  articleno =    "2240013",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
  remark =       "Special Issue on Advances in Deep Learning Algorithms
                 for Brain Imaging Guest Editor: Dr. Bala Anand Muthu",
}

@Article{Suresh:2023:DLC,
  author =       "Gulivindala Suresh and Chanamallu Srinivasa Rao",
  title =        "Detection and Localization of Copy--Move Forgery in
                 Digital Images: Review and Challenges",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467823500250",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500250",
  abstract =     "Copy move forgery in digital images became a common
                 problem due to the wide accessibility of image
                 processing algorithms and open-source editing software.
                 The human visual system cannot identify the traces of
                 forgery in the tampered image. The proliferation of
                 such digital images through the internet and social
                 media is possible with a finger touch. These tampered
                 images have been used in news reports, judicial
                 forensics, medical records, and financial statements.
                 In this paper, a detailed review has been carried on
                 various copy-move forgery detection (CMFD) and
                 localization techniques. Further, challenges in the
                 research are identified along with possible
                 solutions.",
  acknowledgement = ack-nhfb,
  articleno =    "2350025",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Islam:2023:CSD,
  author =       "Md. Shafiqul Islam and Rafiqul Islam",
  title =        "A Critical Survey on Developed Reconstruction
                 Algorithms for Computed Tomography Imaging from a
                 Limited Number of Projections",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500262",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500262",
  abstract =     "Rapid system and hardware development of X-ray
                 computed tomography (CT) technologies has been
                 accompanied by equally exciting advances in image
                 reconstruction algorithms. Of the two reconstruction
                 algorithms, analytical and iterative, iterative
                 reconstruction (IR) algorithms have become a clinically
                 viable option in CT imaging. The first CT scanners in
                 the early 1970s used IR algorithms, but lack of
                 computation power prevented their clinical use. In
                 2009, the first IR algorithms became commercially
                 available and replaced conventionally established
                 analytical algorithms as filtered back projection.
                 Since then, IR has played a vital role in the field of
                 radiology. Although all available IR algorithms share
                 the common mechanism of artifact reduction and/or
                 potential for radiation dose reduction, the magnitude
                 of these effects depends upon specific IR algorithms.
                 IR reconstructs images by iteratively optimizing an
                 objective function. The objective function typically
                 consists of a data integrity term and a regularization
                 term. Therefore, different regularization priors are
                 used in IR algorithms. This paper will briefly look at
                 the overall evolution of CT image reconstruction and
                 the regularization priors used in IR algorithms.
                 Finally, a discussion is presented based on the reality
                 of various reconstruction methodologies at a glance to
                 find the preferred one. Consequently, we will present
                 anticipation towards future advancements in this
                 domain.",
  acknowledgement = ack-nhfb,
  articleno =    "2350026",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Zhiyu:2023:IIA,
  author =       "Wang Zhiyu and Ding Weili and And Wang Mingkui",
  title =        "Illumination Invariance Adaptive Sidewalk Detection
                 Based on Unsupervised Feature Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500274",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500274",
  abstract =     "To solve the problem of road recognition when the
                 robot is driving on the sidewalk, a novel sidewalk
                 detection algorithm from the first-person perspective
                 is proposed, which is crucial for robot navigation. The
                 algorithm starts from the illumination invariance graph
                 of the sidewalk image, and the sidewalk ``seeds'' are
                 selected dynamically to get the sidewalk features for
                 unsupervised feature learning. The final sidewalk
                 region will be extracted by multi-threshold adaptive
                 segmentation and connectivity processing. The key
                 innovations of the proposed algorithm are the method of
                 illumination invariance based on PCA and the
                 unsupervised feature learning for sidewalk detection.
                 With the PCA-based illumination invariance, it can
                 calculate the lighting invariance angle dynamically to
                 remove the impact of illumination and different brick
                 colors' influence on sidewalk detection. Then the
                 sidewalk features are selected dynamically using the
                 parallel geometric structure of the sidewalk, and the
                 confidence region of the sidewalk is obtained through
                 unsupervised feature learning. The proposed method can
                 effectively suppress the effects of shadows and
                 different colored bricks in the sidewalk area. The
                 experimental result proves that the F-measure of the
                 proposed algorithm can reach 93.11\% and is at least
                 7.7\% higher than the existing algorithm.",
  acknowledgement = ack-nhfb,
  articleno =    "2350027",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chole:2023:LMO,
  author =       "Vikrant Chole and Vijay Gadicha",
  title =        "Locust Mayfly Optimization-Tuned Neural Network for
                 {AI}-Based Pruning in Chess Game",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500286",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500286",
  abstract =     "The art of mimicking a human's responses and behavior
                 in a programming machine is called Artificial
                 intelligence (AI). AI has been incorporated in games in
                 such a way to make them interesting, especially in
                 chess games. This paper proposes a hybrid optimization
                 tuned neural network (NN) to establish a winning
                 strategy in the chess game by generating the possible
                 next moves in the game. Initially, the images from
                 Portable Game Notation (PGN) file are used to train the
                 NN classifier. The proposed Locust Mayfly algorithm is
                 utilized to optimally tune the weights of the NN
                 classifier. The proposed Locust Mayfly algorithm
                 inherits the characteristic features of hybrid survival
                 and social interacting search agents. The NN classifier
                 involves in finding all the possible moves in the
                 board, among which the best move is obtained using the
                 mini-max algorithm. At last, the performance of the
                 proposed Locust mayfly-based NN method is evaluated
                 with help of the performance metrics, such as
                 specificity, accuracy, and sensitivity. The proposed
                 Locust mayfly-based NN method attained a specificity of
                 98\%, accuracy of 98\%, and a sensitivity of 98\%,
                 which demonstrates the productiveness of the proposed
                 mayfly-based NN method in pruning.",
  acknowledgement = ack-nhfb,
  articleno =    "2350028",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pankaj:2023:NDO,
  author =       "Pankaj and P. K. Bharti and And Brajesh Kumar",
  title =        "A New Design of Occlusion-Invariant Face Recognition
                 Using Optimal Pattern Extraction and {CNN} with
                 {GRU}-Based Architecture",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500298",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500298",
  abstract =     "Face detection is a computer technology being used in
                 a variety of applications that identify human faces in
                 digital images. In many face recognition challenges,
                 Convolutional Neural Networks (CNNs) are regarded as a
                 problem solver. Occlusion is determined as the most
                 common challenge of face recognition in realistic
                 applications. Several studies are undergoing to obtain
                 face recognition without any challenges. However, the
                 occurrence of noise and occlusion in the image reduces
                 the achievement of face recognition. Hence, various
                 researches and studies are carried out to solve the
                 challenges involved with the occurrence of occlusion
                 and noise in the image, and more clarification is
                 needed to acquire high accuracy. Hence, a deep learning
                 model is intended to be developed in this paper using
                 the meta-heuristic approach. The proposed model covers
                 four main steps: (a) data acquisition, (b)
                 pre-processing, (c) pattern extraction and (d)
                 classification. The benchmark datasets regarding the
                 face image with occlusion are gathered from a public
                 source. Further, the pre-processing of the images is
                 performed by contrast enhancement and Gabor filtering.
                 With these pre-processed images, pattern extraction is
                 done by the optimal local mesh ternary pattern. Here,
                 the hybrid Whale--Galactic Swarm Optimization (WGSO)
                 algorithm is used for developing the optimal local mesh
                 ternary pattern extraction. By inputting the
                 pattern-extracted image, the new deep learning model
                 namely ``CNN with Gated Recurrent Unit (GRU)'' network
                 performs the recognition process to maximize the
                 accuracy, and also it is used to enhance the face
                 recognition model. From the results, in terms of
                 accuracy, the proposed WGSO- CNN+GRU model is better by
                 4.02\%, 3.76\% and 2.17\% than the CNN, SVM and SRC,
                 respectively. The experimental results are presented by
                 performing their comparative analysis on a standard
                 dataset, and they assure the efficiency of the proposed
                 model. However, many challenging problems related to
                 face recognition still exist, which offer excellent
                 opportunities to face recognition researchers in the
                 future.",
  acknowledgement = ack-nhfb,
  articleno =    "2350029",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rahul:2023:ESP,
  author =       "Vaddadi Sai Rahul and M. Tejas and N. Narayanan
                 Prasanth and And S. P. Raja",
  title =        "Early Success Prediction of {Indian} Movies Using
                 Subtitles: a Document Vector Approach",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500304",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500304",
  abstract =     "Scientific studies of the elements that influence the
                 box office performance of Indian films have generally
                 concentrated on post-production elements, such as those
                 discovered after a film has been completed or released,
                 and notably for Bollywood films. Only fewer studies
                 have looked at regional film industries and
                 pre-production factors, which are elements that are
                 known before a decision to greenlight a film is made.
                 This study looked at Indian films using natural
                 language processing and machine learning approaches to
                 see if they would be profitable in the pre-production
                 stage. We extract movie data and English subtitles (as
                 an approximation to the screenplay) for the top five
                 Indian regional film industries: Bollywood, Kollywood,
                 Tollywood, Mollywood, and Sandalwood, as they make up a
                 major portion of the Indian film industry's revenue.
                 Subtitle Vector (Sub2Vec), a Paragraph Vector model
                 trained on English subtitles, was used to embed
                 subtitle text into 50 and 100 dimensions. The proposed
                 approach followed a two-stage pipeline. In the first
                 stage, Return on Investment (ROI) was calculated using
                 aggregated subtitle embeddings and associated movie
                 data. Classification models used the ROI calculated in
                 the first step to predicting a film's verdict in the
                 second step. The optimal regressor--classifier pair was
                 determined by evaluating classification models using $
                 F_1$-score and Cohen's Kappa scores on various
                 hyperparameters. When compared to benchmark methods,
                 our proposed methodology forecasts box office success
                 more accurately.",
  acknowledgement = ack-nhfb,
  articleno =    "2350030",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Honnutagi:2023:UVE,
  author =       "Pooja Honnutagi and Y. S. Laitha and And V. D. Mytri",
  title =        "Underwater Video Enhancement Using Manta Ray Foraging
                 Lion Optimization-Based Fusion Convolutional Neural
                 Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500316",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500316",
  abstract =     "Due to the significance of aquatic robotics and marine
                 engineering, the underwater video enhancement has
                 gained huge attention. Thus, a video enhancement
                 method, namely Manta Ray Foraging Lion
                 Optimization-based fusion Convolutional Neural Network
                 (MRFLO-based fusion CNN) algorithm is developed in this
                 research for enhancing the quality of the underwater
                 videos. The MRFLO is developed by merging the Lion
                 Optimization Algorithm (LOA) and Manta Ray Foraging
                 Optimization (MRFO). The blur in the input video frame
                 is detected and estimated through the Laplacian's
                 variance method. The fusion CNN classifier is used for
                 deblurring the frame by combining both the input frame
                 and blur matrix. The fusion CNN classifier is tuned by
                 the developed MRFLO algorithm. The pixel of the
                 deblurred frame is enhanced using the Type II Fuzzy
                 system and Cuckoo Search optimization algorithm filter
                 (T2FCS filter). The developed MRFLO-based fusion CNN
                 algorithm uses the metrics, Underwater Image Quality
                 Measure (UIQM), Underwater Color Image Quality
                 Evaluation (UCIQE), Structural Similarity Index Measure
                 (SSIM), Mean Square Error (MSE), and Peak
                 Signal-to-Noise Ratio (PSNR) for the evaluation by
                 varying the blur intensity. The proposed MRFLO-based
                 fusion CNN algorithm acquired a PSNR of 38.9118, SSIM
                 of 0.9593, MSE of 3.2214, UIQM of 3.0041 and UCIQE of
                 0.7881.",
  acknowledgement = ack-nhfb,
  articleno =    "2350031",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sasikaladevi:2023:CBD,
  author =       "N. Sasikaladevi and A. Revathi",
  title =        "Certainty-Based Deep Fused Neural Network Using
                 Transfer Learning and Adaptive Movement Estimation for
                 the Diagnosis of Cardiomegaly",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S021946782350033X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782350033X",
  abstract =     "Cardiomegaly is a radiographic abnormality, and it has
                 significant prognosis importance in the population.
                 Chest X-ray images can identify it. Early detection of
                 cardiomegaly reduces the risk of congestive heart
                 failure and systolic dysfunction. Due to the lack of
                 radiologists, there is a demand for the artificial
                 intelligence tool for the early detection of
                 cardiomegaly. The cardiomegaly X-ray dataset is
                 extracted from the cheXpert database. Totally, 46195
                 X-ray records with a different view such as AP view, PA
                 views, and lateral views are used to train and validate
                 the proposed model. The artificial intelligence app
                 named CardioXpert is constructed based on deep neural
                 network. The transfer learning approach is adopted to
                 increase the prediction metrics, and an optimized
                 training method called adaptive movement estimation is
                 used. Three different transfer learning-based deep
                 neural networks named APNET, PANET, and LateralNET are
                 constructed for each view of X-ray images. Finally,
                 certainty-based fusion is performed to enrich the
                 prediction accuracy, and it is named CardioXpert. As
                 the proposed method is based on the largest
                 cardiomegaly dataset, hold-out validation is performed
                 to verify the prediction accuracy of the proposed
                 model. An unseen dataset validates the model. These
                 deep neural networks, APNET, PANET, and LateralNET, are
                 individually validated, and then the fused network
                 CardioXpert is validated. The proposed model
                 CardioXpert provides an accuracy of 93.6\%, which is
                 the highest at this time for this dataset. It also
                 yields the highest sensitivity of 94.7\% and a
                 precision of 97.7\%. These prediction metrics prove
                 that the proposed model outperforms all the
                 state-of-the-art deep transfer learning methods for
                 diagnosing cardiomegaly thoracic disorder. The proposed
                 deep learning neural network model is deployed as the
                 web app. The cardiologist can use this prognostic app
                 to predict cardiomegaly disease faster and more robust
                 in the early state by using low-cost and chest X-ray
                 images.",
  acknowledgement = ack-nhfb,
  articleno =    "2350033",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ainapure:2023:DEM,
  author =       "Bharati S. Ainapure and Mythili Boopathi and Chandra
                 Sekhar Kolli and And C. Jackulin",
  title =        "Deep Ensemble Model for Spam Classification in
                 {Twitter} via Sentiment Extraction:
                 Bio-Inspiration-Based Classification Model",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500341",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500341",
  abstract =     "Twitter Spam has turned out to be a significant
                 predicament of these days. Current works concern on
                 exploiting the machine learning models to detect the
                 spams in Twitter by determining the statistic features
                 of the tweets. Even though these models result in
                 better success, it is hard to sustain the performances
                 attained by the supervised approaches. This paper
                 intends to introduce a deep learning-assisted spam
                 classification model on twitter. This classification is
                 based on sentiments and topics modeled in it. The
                 initial step is data collection. Subsequently, the
                 collected data are preprocessed with ``stop word
                 removal, stemming and tokenization''. The next step is
                 feature extraction, wherein, the post tagging,
                 headwords, rule-based lexicon, word length, and
                 weighted holoentropy features are extracted. Then, the
                 proposed sentiment score extraction is carried out to
                 analyze their variations in nonspam and spam
                 information. At last, the diffusions of spam data on
                 Twitter are classified into spam and nonspams. For
                 this, an Optimized Deep Ensemble technique is
                 introduced that encloses ``neural network (NN), support
                 vector machine (SVM), random forest (RF) and
                 convolutional neural network (DNN)''. Particularly, the
                 weights of DNN are optimally tuned by an arithmetic
                 crossover-based cat swarm optimization (AC-CS) model.
                 At last, the supremacy of the developed approach is
                 examined via evaluation over extant techniques.
                 Accordingly, the proposed AC-CS + ensemble model
                 attained better accuracy value when the learning
                 percentage is 80, which is 18.1\%, 14.89\%, 11.7\%,
                 12.77\%, 10.64\%, 6.38\%, 6.38\%, and 6.38\% higher
                 than SVM, DNN, RNN, DBN, MFO + ensemble model, WOA +
                 ensemble model, EHO + ensemble model and CSO + ensemble
                 model models.",
  acknowledgement = ack-nhfb,
  articleno =    "2350034",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lata:2023:DDL,
  author =       "Navdeep Lata and Raman Kumar",
  title =        "{DSIT}: a Dynamic Lightweight Cryptography Algorithm
                 for Securing Image in {IoT} Communication",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500353",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500353",
  abstract =     "One of the most significant challenges appears to be
                 securing the Internet of Things (IoT) communication
                 network. As a corollary, information security has
                 become the basis for establishing trustworthiness in
                 IoT network communication. Cryptography is one of the
                 ways for securing information in this case. However,
                 the majority of current approaches are static, making
                 them subject to security threats. As a consequence, a
                 new concept, dynamic encryption, is growing rapidly in
                 IoT communication. In this paper, a dynamic encryption
                 algorithm (DSIT) has been proposed to secure IoT
                 communication. This algorithm is based on Feistel and
                 Substitution--Permutation Network. DSIT is a block
                 cipher that takes the 64-bit block of plaintext, 64-bit
                 secret key, and a secret dynamic box (D-box) as input.
                 It produces a 64-bit ciphertext by performing eight
                 rounds of the DSIT algorithm. For each round, the key
                 and D-box are updated. This dynamic effect provides
                 high security to a dynamic IoT network. The proposed
                 algorithm has been executed in IoT environment using
                 Raspberry Pi 3 Model B + and 50\% average Avalanche
                 effect has been achieved. The proposed algorithm
                 efficiently encrypts the image data to secure the
                 communication and high resistant to cryptanalysis
                 attacks.",
  acknowledgement = ack-nhfb,
  articleno =    "2350035",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dahiya:2023:RDL,
  author =       "Neelam Dahiya and Sartajvir Singh and And Sheifali
                 Gupta",
  title =        "A Review on Deep Learning Classifier for Hyperspectral
                 Imaging",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500365",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500365",
  abstract =     "Nowadays, hyperspectral imaging (HSI) attracts the
                 interest of many researchers in solving the remote
                 sensing problems especially in various specific domains
                 such as agriculture, snow/ice, object detection and
                 environmental monitoring. In the previous literature,
                 various attempts have been made to extract the critical
                 information through hyperspectral imaging which is not
                 possible through multispectral imaging (MSI). The
                 classification in image processing is one of the
                 important steps to categorize and label the pixels
                 based on some specific rules. There are various
                 supervised and unsupervised approaches which can be
                 used for classification. Since the past decades,
                 various classifiers have been developed and improved to
                 meet the requirement of remote sensing researchers.
                 However, each method has its own merits and demerits
                 and is not applicable in all scenarios. Past literature
                 also concluded that deep learning classifiers are more
                 preferable as compared to machine learning classifiers
                 due to various advantages such as lesser training time
                 for model generation, handle complex data and lesser
                 user intervention requirements. This paper aims to
                 perform the review on various machine learning and deep
                 learning-based classifiers for HSI classification along
                 with challenges and remedial solution of deep learning
                 with hyperspectral imaging. This work also highlights
                 the various limitations of the classifiers which can be
                 resolved with developments and incorporation of
                 well-defined techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "2350036",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Birajdar:2023:SSP,
  author =       "Gajanan K. Birajdar and Mukesh D. Patil",
  title =        "A Systematic Survey on Photorealistic Computer Graphic
                 and Photographic Image Discrimination",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500377",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Aug 5 16:18:20 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500377",
  abstract =     "The advent in graphic rendering software and
                 technological progress in hardware can generate or
                 modify photorealistic computer graphic (CG) images that
                 are difficult to identify by human observers.
                 Computer-generated images are used in magazines, film
                 and advertisement industry, medical and insurance
                 agencies, social media, and law agencies as an
                 information carrier. The forged computer-generated
                 image created by the malicious user may distort social
                 stability and impacts on public opinion. Hence, the
                 precise identification of computer graphic and
                 photographic image (PG) is a significant and
                 challenging task. In the last two decades, several
                 researchers have proposed different algorithms with
                 impressive accuracy rate, including a recent addition
                 of deep learning methods. This comprehensive survey
                 presents techniques dealing with CG and PG image
                 classification using machine learning and deep
                 learning. In the beginning, broad classification of all
                 the methods in to five categories is discussed in
                 addition to generalized framework of CG detection.
                 Subsequently, all the significant works are surveyed
                 and are grouped into five types: image statistics
                 methods, acquisition device properties-based
                 techniques, color, texture, and geometry-based methods,
                 hybrid methods, and deep learning methods. The
                 advantages and limitations of CG detection methods are
                 also presented. Finally, major challenges and future
                 trends in the CG and PG image identification field are
                 discussed.",
  acknowledgement = ack-nhfb,
  articleno =    "2350037",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Ravikumar:2023:AMS,
  author =       "S. Ravikumar and E. Kannan",
  title =        "Analysis on Mental Stress of Professionals and
                 Pregnant Women Using Machine Learning Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467823500389",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500389",
  abstract =     "Stress is the way that everyone can respond actually,
                 intellectually and sincerely to different conditions,
                 changes and requests in our lives. Stress problems are
                 a typical issue among working experts in the business
                 today. With changing way of life and work societies,
                 there is an expansion in the stress among the
                 representatives. However, numerous ventures and
                 corporate give emotional wellness-related plans and
                 attempt to facilitate the work environment climate, the
                 issue is a long way from control. When it comes to
                 Pregnant Women, the uterus climate assumes a
                 fundamental part in future development and improvement
                 of hatchling. Stress during pregnancy will influence
                 the sensitive climate of the hatchling. These can
                 remember impacts for your unborn child's development
                 and the length of incubation period. They can likewise
                 expand the danger of issues in your child's future
                 physical and mental turn of events, just as social
                 issues in youth. By using various machine learning
                 techniques, the proposed model can analyze the stress
                 in a working professional and also in a pregnant woman.
                 We can predict the best way of yoga to reduce their
                 stress and get good work results from working employees
                 and a good growth in fetus of a pregnant women. Yoga
                 can positively affect the parasympathetic sensory
                 system and helps in bringing down heartbeat and
                 circulatory strain. This decreases the interest of the
                 body for oxygen and furthermore increment lung limit.
                 Compelling utilization of yoga can likewise decrease
                 the odds of stress, nervousness and despondency.",
  acknowledgement = ack-nhfb,
  articleno =    "2350038",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sadaghiani:2023:IIB,
  author =       "Abdol Vahab Khalili Sadaghiani and Samad Sheikhaei and
                 And Behjat Forouzandeh",
  title =        "Image Interpolation Based on {$2$D-DWT} with Novel
                 Regularity-Preserving Algorithm Using {RLS} Adaptive
                 Filters",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500390",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500390",
  abstract =     "This paper proposes a novel method for the image
                 interpolation problem based on two-dimensional discrete
                 wavelet transform (DWT) with the edge preserving
                 approach. The purpose of this method is to consider two
                 contrasting issues of over-smoothing and creation of
                 spurious edges at the same time, and offer a novel
                 solution based on statistical dependencies of image
                 sub-bands, and noise behavior. The offered method has a
                 multi-faceted approach for the problem; by sub-band
                 coding, it handles each 2D-DWT image sub-band with a
                 different solution. For LH and HL sub-bands, two
                 algorithms work together in order to preserve
                 regularity. Area\_Check algorithm is a four-phase
                 edge-preserving algorithm that aims to recognize and
                 interpolate separating lines of environments and edgy
                 regions in the best possible way. On the other hand,
                 RLS\_AVG algorithm interpolates smooth surfaces of the
                 image by keeping the regularity of the image without
                 over-smoothing. In this regard, the offered algorithm
                 has a great power to counter jaggies and annoying
                 artifacts. In the end, in order to demonstrate the
                 capability, and performance of the proposed method, the
                 final results in various metrics are compared with the
                 results of the most famous and the newest image
                 interpolation methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2350039",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gill:2023:NIG,
  author =       "Jasmeen Gill and Ravinder Pal Singh",
  title =        "Non-Invasive Grading and Sorting of Mango
                 (\bioname{Mangiferad indica} {L.}) Using Antlion
                 Optimizer-Based Artificial Neural Networks",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500407",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500407",
  abstract =     "Mango is an imperative commercial fruit in terms of
                 market value and volume of production. In addition, it
                 is grown in more than ninety nations around the globe.
                 Consequently, the demand for effective grading and
                 sorting has increased, ever since. This communication
                 describes a non-invasive mango fruit grading and
                 sorting model that utilizes hybrid soft computing
                 approach. Artificial neural networks (ANN), optimized
                 with Antlion optimizer (ALO), are used as a
                 classification tool. The quality of mangoes is
                 evaluated according to four grading parameters: size
                 (volume and morphology), maturity (ripe/unripe), defect
                 (defective/healthy) and variety (cultivar). Besides, a
                 comparison of proposed grading system with
                 state-of-the-art models is performed. The system showed
                 an overall classification rate of 95.8\% and
                 outperformed the other models. Results demonstrate the
                 effectiveness of proposed model in fruit grading and
                 sorting applications.",
  acknowledgement = ack-nhfb,
  articleno =    "2350040",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Padmavathi:2023:WCE,
  author =       "P. Padmavathi and J. Harikiran",
  title =        "Wireless Capsule Endoscopy Infected Images Detection
                 and Classification Using {MobileNetV2-BiLSTM} Model",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500419",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500419",
  abstract =     "An efficient tool to execute painless imaging and
                 examine gastrointestinal tract illnesses of the
                 intestine is also known as wireless capsule endoscopy
                 (WCE). Performance, safety, tolerance, and efficacy are
                 the several concerns that make adaptation challenging
                 and wide applicability. In addition, to detect
                 abnormalities, the great importance is the automatic
                 analysis of the WCE dataset. These issues are resolved
                 by numerous vision-based and computer-aided solutions.
                 But, they want further enhancements and do not give the
                 accuracy at the desired level. In order to solve these
                 issues, this paper presents the detection and
                 classification of WCE infected images by a deep neural
                 network and utilizes a bleed image recognizer (BIR)
                 that associates the MobileNetV2 design to classify the
                 images of WCE infected. For the opening-level
                 evaluation, the BIR uses the MobileNetV2 model for its
                 minimum computation power necessity, and then the
                 outcome is sent to the CNN for more processing. Then,
                 Bi-LSTM with an attention mechanism is used to improve
                 the performance level of the model. Hybrid attention
                 Bi-LSTM design yields more accurate classification
                 outcomes. The proposed scheme is implemented in the
                 Python platform and the performance is evaluated by
                 Cohen's kappa, F1-score, recall, accuracy, and
                 precision. The implementation outcomes show that the
                 introduced scheme achieved maximum accuracy of 0.996
                 with data augmentation with the dataset of WCE images
                 which provided higher outcomes than the others.",
  acknowledgement = ack-nhfb,
  articleno =    "2350041",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rajkumar:2023:DLF,
  author =       "Rajeev Rajkumar",
  title =        "Deep Learning Feature Extraction Using Attention-Based
                 {DenseNet 121} for Copy Move Forgery Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500420",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500420",
  abstract =     "In modern society, digital images can be far-reaching,
                 and the images are manipulated by various software and
                 hardware technologies. The image forgery activities are
                 undertaken by the attackers mainly for damaging the
                 reputation of people or receiving fiscal gain, etc.
                 Taking this into consideration, many techniques are
                 developed to detect the forged images. In this paper, a
                 new deep learning-based approach is introduced for
                 copy-move forgery detection. The input images are
                 segmented into non-overlapping patches using
                 superpixel-based modified dense peak clustering and the
                 features are extracted from the segmented patches by
                 applying deep learning structure of attention-based
                 DenseNet 121 model. Besides, to compare every block,
                 the depth of each pixel is reconstructed, and
                 eventually matching process is carried out using the
                 adaptive chimp patch matching approach, which detects
                 the suspicious forged regions in an image. Finally, the
                 matched keypoints are merged with the segmented patches
                 using the merged keypoint matching algorithm. As a
                 result, the new deep learning approach has detected the
                 forged regions efficiently from the tampered image with
                 less time even the image is compressed, rotated, or
                 scaled. The performance is evaluated in terms of
                 recall, precision, accuracy, F1-score, computational
                 time, and False Positive Rate (FPR). Moreover, the
                 performance is compared with the other existing
                 approaches, and the outcomes showed that the proposed
                 method has achieved higher accuracy of 97\%, recall of
                 99\%, precision of 97.84\%, F1-score of 98.81\%, FPR of
                 0.022 and less computational time of 2.5 s.",
  acknowledgement = ack-nhfb,
  articleno =    "2350042",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kaur:2023:FIB,
  author =       "Rajdeep Kaur and Rakesh Kumar and And Meenu Gupta",
  title =        "Food Image-based Nutritional Management System to
                 Overcome Polycystic Ovary Syndrome using
                 {DeepLearning}: A Systematic Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500432",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500432",
  abstract =     "Polycystic Ovary Syndrome (PCOS) is one of the growing
                 non-communicable diseases in those women who do not
                 take proper nutrients in their meals. Medically, it is
                 not proven that an unhealthy diet is the only cause of
                 PCOS, but it is one of the major causes behind this
                 disease. PCOS is an endocrine disorder that influences
                 8--10\% of women at their reproductive age and may
                 cause infertility or other health problems. Deep
                 Learning (DL) is a popular technique to classify the
                 food images for identifying the nutrients in the food.
                 This work considers food image datasets (FOOD-101,
                 UEC-256, UEC-100, etc.) to analyze the food image using
                 pre-trained Convolutional Neural Network (CNN) and a
                 nutritional information dataset for identifying the
                 nutrients in food. The proposed study aims to find the
                 solution to overcome the PCOS problem in women by
                 tracking nutrient intake using food images and
                 recommending the diet. Further, this study will also
                 provide comprehensive review of image classification
                 and recommendation techniques that may help the
                 dieticians to track the nutrient intake using food
                 images provided by PCOS patients to overcome the
                 disease.",
  acknowledgement = ack-nhfb,
  articleno =    "2350043",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Nnolim:2023:FOP,
  author =       "Uche A. Nnolim",
  title =        "Fourth-Order Partial Differential Equation Framelet
                 Fusion-Based Colour Correction and Contrast Enhancement
                 for Underwater Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500444",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500444",
  abstract =     "A framelet augmented fourth-order forward-reverse
                 partial differential equation (PDE)-fusion-based
                 algorithm is proposed for underwater image enhancement.
                 The algorithm combines framelet domain transform-based
                 fusion of modified base, detail and amplified detail
                 layers in a PDE-based formulation. The extracted layers
                 via framelet decomposition with adaptive threshold
                 computation comprise the detail and approximation
                 components of the images, which are amplified,
                 attenuated and aggregated. Additions include a modified
                 global contrast enhancement/color correction function
                 and a suitable color space transformation to enhance
                 difficult underwater images with flat non-overlapping
                 color channel histograms. Also, gradient domain fusion
                 of several color corrected image layers and fuzzy
                 rule-based enhancement is combined in the proposed
                 PDE-based fusion framework. Furthermore, variational
                 illumination correction was also employed for better
                 enhancement of dark underwater images. Experimental
                 comparisons indicate that the proposed approaches yield
                 better overall visual and numerical results in most
                 cases when compared with state-of-the-art methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2350044",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gavade:2023:HFD,
  author =       "Priyanka A. Gavade and Vandana S. Bhat and And
                 Jagadeesh Pujari",
  title =        "Hybrid Features and Deep Learning Model for Facial
                 Expression Recognition From Videos",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500456",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500456",
  abstract =     "Facial expression recognition plays a crucial function
                 in the advancement of technologies that can be used in
                 detecting mental illness, sensors, and a wide variety
                 of applications. Facial expression recognition is an
                 interesting as well as strenuous task in digital field
                 due to the complexity of the varying individuals. The
                 intention of this work is to establish a face
                 recognition model relying upon the modified GWO-based
                 ensemble deep convolutional neural network (DCNN),
                 which effectively recognizes the expressions. The
                 substance of the research anticipates on the proposed
                 modified GWO optimization which helps in maintaining
                 the storage capacity with simple structures and
                 provides high convergence. Enabling the optimization in
                 the ensemble DCNN helps in tuning the internal
                 parameters present in the classifier as well as helps
                 in attaining best solution. The accomplishment of the
                 proposed expression recognition model is evaluated
                 utilizing the parameter metrics accuracy, precision,
                 and recall that attained the values of 94.114\%,
                 92.003\%, and 95.734\% which is more efficient.",
  acknowledgement = ack-nhfb,
  articleno =    "2350045",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Vishnuvardhan:2023:MIF,
  author =       "Veruva Vishnuvardhan and T. Jaya",
  title =        "Medical Image Fusion using {ECNN}- and {OMBO}-based
                 Adaptive Weighted Fusion Rule",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500468",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500468",
  abstract =     "Medical imaging and information processing
                 technologies are constantly evolving, resulting in a
                 wide range of multimodality therapeutic pictures for
                 clinical illness investigation. Physicians often
                 require medical images produced using various
                 modalities such as computed tomography (CT), magnetic
                 resonance (MR), and positron emission computed
                 tomography (PET) for clinical diagnosis. Many deep
                 learning-based fusion methods have recently been
                 proposed. In Convolutional Neural Network (CNN)-based
                 fusion methods, only the last layer results are used as
                 the image features, which result in the loss of useful
                 information at middle layers. The fusion rule, based on
                 the weighted averaging, causes noises in the source
                 images and suppresses salient features of the image. In
                 order to solve these issues, this paper proposes
                 medical image fusion using Enhanced CNN (ECNN)- and
                 Opposition-based Monarch Butterfly Optimization
                 (OMBO)-based adaptive weighted fusion rule (AWFR). The
                 ECNN contains feature extraction and reconstruction
                 components. Both these components are trained in order
                 to minimize the pixel loss and structural similarity
                 loss. A pair of multimodal medical image is passed as
                 input to the ECNN model to extract the low level and
                 high level features. For the extracted features from
                 ECNN, weighted fusion rule is applied in which OMBO
                 algorithm is applied to adaptively optimize the weights
                 of the fusion rule.",
  acknowledgement = ack-nhfb,
  articleno =    "2350046",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jameel:2023:BAI,
  author =       "Samer Kais Jameel and Jafar Majidpour",
  title =        "{BCS-AE}: Integrated Image Compression-Encryption
                 Model Based on {AE} and {Block-CS}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S021946782350047X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782350047X",
  abstract =     "For Compressive Sensing problems, a number of
                 techniques have been introduced, including traditional
                 compressed-sensing (CS) image reconstruction and Deep
                 Neural Network (DNN) models. Unfortunately, due to low
                 sampling rates, the quality of image reconstruction is
                 still poor. This paper proposes a lossy image
                 compression model (i.e. BCS-AE), which combines two
                 different types to produce a model that uses more
                 high-quality low-bitrate CS reconstruction. Initially,
                 block-based compressed sensing (BCS) was utilized, and
                 it was done one block at a time by the same operator.
                 It can correctly extract images with complex geometric
                 configurations. Second, we create an AutoEncoder
                 architecture to replace traditional transforms, and we
                 train it with a rate-distortion loss function. The
                 proposed model is trained and then tested on the CelebA
                 and Kodak databases. According to the results, advanced
                 deep learning-based and iterative optimization-based
                 algorithms perform better in terms of compression ratio
                 and reconstruction quality.",
  acknowledgement = ack-nhfb,
  articleno =    "2350047",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Preetham:2023:SMR,
  author =       "Anusha Preetham and Vishnu Vardhan Battu",
  title =        "Soil Moisture Retrieval Using Sail Squirrel Search
                 Optimization-based Deep Convolutional Neural Network
                 with {Sentinel-1} Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500481",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500481",
  abstract =     "Soil Moisture (SM) is an environmental descriptor,
                 which acts as the affiliation between the atmosphere
                 and the earth's surface. Various SM retrieval methods
                 are developed to abolish the influence of vegetation
                 cover attenuation, surface roughness, and scattering to
                 find an association among SM and backscatter
                 coefficient. To understand the relationship between
                 various vegetation parameters and backscatter
                 coefficient poses a great challenge in SM retrieval.
                 Hence, an efficacious SM retrieval method is afforded
                 using the proposed Sail Squirrel Search
                 Optimization-based Deep Convolutional Neural Network
                 (SSSO-based Deep CNN). Here, the proposed SSSO is
                 derived by concatenating the Sail Fish Optimization
                 (SFO) with Squirrel Search Algorithm (SSA). The Deep
                 CNN performs the process of SM retrieval using
                 vegetation indices. The fitness measure of the proposed
                 optimization enables to find the best solution to
                 update the weights of the classifier for increasing the
                 efficiency of the retrieval mechanism. By training Deep
                 CNN with the proposed optimization, the soil moisture
                 of an area is effectively retrieved. However, the
                 proposed SSSO-based Deep CNN obtained minimal
                 estimation error and minimal RMSE of 0.550 and 0.726
                 using sentinel-1 data, respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2350048",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gantenapalli:2023:SMF,
  author =       "Srinivasa Rao Gantenapalli and Praveen Babu Choppala
                 and And James Stephen Meka",
  title =        "Selective Mean Filtering for Reducing Impulse Noise in
                 Digital Color Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500493",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500493",
  abstract =     "The interest of this paper is in reduction of impulse
                 noise in digital color images. The two main methods
                 used for noise reduction in images are the mean and
                 median filters. These techniques operate by replacing
                 the test pixel in a chosen window by a new filtered
                 pixel value. The window is made to iteratively slide
                 across the entire image to reconstruct a new noise
                 reduced image. The mean filters suffer from the effect
                 of smoothing out color contrast and edges due to
                 leveraging the unrepresentative pixels in the filtering
                 process. The vector median filter and its variants
                 overcome this problem by considering only the most
                 representative pixel in the chosen window. The most
                 representative pixel, i.e. the pixel that is of highest
                 conformity to take the place of the test pixel, is
                 determined by minimizing the aggregate distance from
                 one pixel to every other pixel in the window. The
                 problem in these median filtering approaches is that
                 only one pixel is treated as representative of all the
                 pixels in the chosen window. This conjecture could lead
                 to information loss due to marginalizing other pixels
                 that also are representative of the center pixel. In
                 this paper, we propose a selective mean filtering
                 process to overcome the said problem. The key idea here
                 is to determine the most representative pixels in the
                 window using the method of aggregate distances and then
                 compute the mean of these pixels. This approach will
                 perform better than the vector median filters as now a
                 set of representative pixels are leveraged into the
                 filtering process. Simulation results show that the
                 proposed method performs better than the conventional
                 vector median filtering methods in terms of noise
                 reduction and structural similarity and thus validates
                 the proposed approach. Moreover, the method is tested
                 on real MRI scan images in successfully reducing
                 impulse noise for improved medical diagnosis.",
  acknowledgement = ack-nhfb,
  articleno =    "2350049",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wang:2023:TIN,
  author =       "Xin Wang and Xiaogang Dong",
  title =        "Time Image De-Noising Method Based on Sparse
                 Regularization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "05",
  pages =        "??--??",
  month =        sep,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467825500093",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Fri Oct 13 07:20:29 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467825500093",
  abstract =     "The blurring of texture edges often occurs during
                 image data transmission and acquisition. To ensure the
                 detailed clarity of the drag-time images, we propose a
                 time image de-noising method based on sparse
                 regularization. First, the image pixel sparsity index
                 is set, and then an image de-noising model is
                 established based on sparse regularization processing
                 to obtain the neighborhood weights of similar image
                 blocks. Second, a time image de-noising algorithm is
                 designed to determine whether the coding coefficient
                 reaches the standard value, and a new image de-noising
                 method is obtained. Finally, the images of electronic
                 clocks and mechanical clocks are used as two kinds of
                 time images to compare different image de-noising
                 methods, respectively. The results show that the
                 sparsity regularization method has the highest peak
                 signal-to-noise ratio among the six compared methods
                 for different noise standard deviations and two time
                 images. The image structure similarity is always above
                 which shows that the proposed method is better than the
                 other five image de-noising methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2550009",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Salman:2023:FCI,
  author =       "Khalid A. Salman and Khalid Shaker and And Sufyan
                 Al-janabi",
  title =        "Fake Colorized Image Detection Approaches: a Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946782350050X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782350050X",
  abstract =     "Colorization is a process used in image editing in
                 which grayscale images are colored with realistic
                 colors. Modern techniques of colorization could produce
                 artfully colored images in such a way that it is
                 difficult for human eyes to differentiate between
                 actual and fake colorized images. As a result,
                 identifying fraudulent colored pictures has captured
                 the scientific community's attention in digital
                 forensics. This paper provides an overview of the
                 strategies used for detecting fake colorized images.
                 Mainly, two approaches were used to design fake
                 colorized image detection systems. The first one uses
                 traditional machine learning (ML) techniques that rely
                 on hand-crafted features derived from images and used
                 to differentiate actual and fake images. The second
                 approach uses deep learning (DL) techniques as ``end to
                 end'' systems that don't have to be supplied with such
                 hand-crafted features, as they can learn the features
                 from the image directly. This paper focuses on the
                 various methods and techniques used in fake-colorized
                 image detection. It may aid researchers in better
                 understanding the benefits and drawbacks of existing
                 technologies to develop more efficient systems in this
                 field.",
  acknowledgement = ack-nhfb,
  articleno =    "2350050",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Singh:2023:RML,
  author =       "Shaminder Singh and Anuj Kumar Gupta and And Tanvi
                 Arora",
  title =        "A Review of Machine Learning-Based Recognition of Sign
                 Language",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500511",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500511",
  abstract =     "Some people in society have impaired cognitive senses
                 like speech and hearing where they cannot behave like
                 normal people. It is quite a complex task for abnormal
                 people to understand as well as recognize the gestures
                 of normal people. This initiates to delve into the
                 study of review of Sign Language Recognition (SLR), in
                 specific to, machine learning techniques. In this work,
                 a review of machine learning techniques based on SLR
                 were portrayed. Several studies related to ML papers
                 have been collected and discussed with their merits and
                 demerits. Thus, the observation dictates that
                 recognition of hand gesture is still a challenging
                 task. There are two sorts of gesture recognition,
                 namely, static and dynamic gesture recognition. Static
                 gesture recognition is developed from the dynamic
                 gesture recognition. Almost, Convolutional Neural
                 Networks (CNNs), Hidden Markov Models (HMM) and
                 Histogram analysis were used as recognition classifiers
                 for sign language. Dynamic gesture recognition process
                 operates on tracking the centroid of hand gesture. It
                 changes the visual information in time basis.
                 Henceforth, study on dynamic gesture recognition needs
                 to be more focused using Machine learning techniques.
                 Comparative analysis is done in perspectives of
                 significance of segmentation models, feature extraction
                 and vision-based approaches.",
  acknowledgement = ack-nhfb,
  articleno =    "2350051",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sharma:2023:VAM,
  author =       "Tejpal Sharma and Dhavleesh Rattan",
  title =        "Visualizing {Android} Malicious Applications Using
                 Texture Features",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500523",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500523",
  abstract =     "Context: Due to the change and advancement in
                 technology, day by day the internet service usages are
                 also increasing. Smartphones have become the necessity
                 for every person these days. It is used to perform all
                 basic daily activities such as calling, SMS, banking,
                 gaming, entertainment, education, etc. Therefore,
                 malware authors are developing new variants of malwares
                 or malicious applications especially for monetary
                 benefits. Objective: Objective of this research paper
                 is to develop a technique that can be used to detect
                 malwares or malicious applications on the android
                 devices that will work for all types of packed or
                 encrypted malicious applications, which usually evade
                 decompiling tools. Method: In the proposed approach,
                 visualization method is used for the detection of
                 malware. In the first phase, application files are
                 converted into images and then in second phase, texture
                 feature of images are extracted using Grey Level
                 Co-occurrence Matrix (GLCM). In the last phase, machine
                 learning classification algorithms are used to classify
                 the malicious and benign applications. Results: The
                 proposed approach is run on different datasets
                 collected from various repositories. Different
                 efficiency parameters are calculated and the proposed
                 approach is compared with the existing approaches.
                 Conclusion: We have proposed a static technique for
                 efficient detection of malwares. The proposed technique
                 performs better than the existing technique.",
  acknowledgement = ack-nhfb,
  articleno =    "2350052",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wankhade:2023:ONN,
  author =       "Megha M. Wankhade and Suvarna S. Chorage",
  title =        "Optimized Neural Network with Refined Features for
                 Categorization of Motor Imaginary Signals",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500535",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500535",
  abstract =     "Motor imaginary (MI) is an attractive research field
                 in the brain--computer interfaces (BCIs) function, in
                 which the system is directed by the imaginary arm
                 movement of the subject. This attention is due to the
                 monstrous potential for its pertinence in
                 neurorestoration, neuroprosthetics, and gaming, where
                 the client's considerations of envisioned developments
                 should be decoded. An electroencephalography (EEG)
                 device is regularly utilized for monitoring frontal
                 cortex movements in BCI frameworks. The EEG signals are
                 perceived through the two fundamental processes such as
                 feature extraction and characterization process. This
                 research concentrates on developing a predominant MI
                 categorization model utilizing deep learning
                 techniques. The prominence of this research relies on
                 the combined features + proposed PROA-based RideNN
                 process known as holo-entropy-based WPD, which extracts
                 the most dominant feature from the EEG signals. The
                 extracted features enhance the performance of the
                 RideNN classifier. The analysis is done by utilizing
                 the BCI Competition-IV-2a, -2b, and GigaScience
                 datasets with respect to performance parameters, such
                 as specificity, accuracy, and sensitivity. The analysis
                 revealed the effective performance of the proposed
                 method with respect to the existing state-of-art
                 methodologies.",
  acknowledgement = ack-nhfb,
  articleno =    "2350053",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yechuri:2023:GAB,
  author =       "Sivaramakrishna Yechuri and Sunny Dayal Vanabathina",
  title =        "Genetic Algorithm-Based Adaptive {Wiener} Gain for
                 Speech Enhancement Using an Iterative Posterior {NMF}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500547",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500547",
  abstract =     "In this paper, we propose a genetic algorithm-based
                 adaptive Wiener gain for speech enhancement using an
                 iterative posterior non-negative matrix factorization
                 (NMF). In the recent past, NMF-based Wiener filtering
                 methods were used to improve the performance of speech
                 enhancement, which has shown that they provide better
                 performance when compared with conventional NMF
                 methods. But performance degrades in non-stationary
                 noise environments. Template-based approaches are more
                 robust and perform better in non-stationary noise
                 environments compared to statistical model-based
                 approaches but are dependent on {\em a priori\/}
                 information. Combining the approaches avoids the
                 drawbacks of both. To improve the performance further,
                 speech and noise bases are adapted simultaneously in
                 the NMF approach. The usage of Super-Gaussian
                 constraints in iterative NMF still improves the
                 performance in non-stationary noise. The silence frame
                 is a challenging task in the case of NMF; still there
                 will be some amount of noise present in those frames.
                 For further enhancement, we have combined with a
                 genetic algorithm (GA)-based adaptive Wiener filter
                 which performs well in denoising and also the GA search
                 the adaptive {\alpha} `` role=''presentation``{$>$}
                 {\textalpha} {\textalpha} {\textalpha} allows us to
                 control the trade-off between fitting the observed
                 spectrogram of mixed speech and noise achieving high
                 likelihood under our prior model. The proposed method
                 outperforms other benchmark algorithms in terms of the
                 source to distortion ratio (SDR), short-time objective
                 intelligibility (STOI), and perceptual evaluation of
                 speech quality (PESQ).",
  acknowledgement = ack-nhfb,
  articleno =    "2350054",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jia:2023:RTM,
  author =       "Baojian Jia and Jie Ren",
  title =        "Real-time Multi-person Pose Tracking Method Using Deep
                 Reinforcement Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500559",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500559",
  abstract =     "To address the problem of low tracking accuracy caused
                 by many recognized objects in the existing methods, we
                 propose a real-time multi-person pose tracking method
                 using deep reinforcement learning. First, the
                 convolutional neural network (CNN) is used to predict
                 the human key points and center vector in grid mode,
                 make the human key points point to the human center
                 according to the center vector, group the human key
                 points according to the distance from the human key
                 points to the human center, complete the multi-person
                 pose estimation, and obtain the human pose sequence
                 diagram. Then, the human pose sequence diagram is input
                 into the deep reinforcement learning network, and the
                 pose label and category label are output by the
                 supervised learning and training stage. The best pose
                 tracking strategy obtained in the reinforcement
                 learning and training stage is applied to online
                 tracking. Finally, CNN is used to predict the
                 rectangular frame position of the pose instead of the
                 target pose, and the tracking is completed when the
                 pose stops. At this time, the rectangular frame
                 position is the result of multi-person pose tracking.
                 The results show that the maximum expected average
                 overlap (EAO) of the proposed method is 0.53. When the
                 root mean square error (RMSE) of the position component
                 threshold reaches 8, the accuracy has been stable at
                 0.98\%. Therefore, the proposed method has high
                 tracking accuracy. In the future, it can be applied to
                 smart home scenarios to realize smart home human pose
                 tracking, effectively identify human dangerous pose and
                 ensure residents' life safety.",
  acknowledgement = ack-nhfb,
  articleno =    "2350055",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sukanya:2023:DLB,
  author =       "S. T. Sukanya and S. Jerine",
  title =        "Deep Learning-Based Melanoma Detection with Optimized
                 Features via Hybrid Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500560",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500560",
  abstract =     "Recently, there had been a massive group of people,
                 who were being rapidly affected by melanoma. Melanoma
                 is a form of skin cancer that develops on the skin's
                 surface layer. This is primarily caused due to
                 excessive skin exposure to UV radiation and severe
                 sunburns. Thus, the early detection of melanoma can aid
                 us to cure it completely. This paper intends to
                 introduce a new melanoma detection framework with four
                 main phases {\em viz.\/} segmentation, feature
                 extraction, optimal feature selection, as well as
                 detection. Initially, the segmentation process takes
                 place to the input skin image {\em via\/} Fuzzy C-Means
                 Clustering (FCM) approach. From the segmented image $ I
                 m_{\rm seg} $ (Imseg), some of the features such as
                 Gray Level Run Length Matrix (GLRM), Local Vector
                 Pattern (LVP), Local Binary Pattern (LBP), Local
                 Directional Pattern (LDP) and Local Tetra Pattern
                 (LTrP) are extracted. As the extracted features $F$ (F)
                 suffered from the issue of ``curse of dimensionality'',
                 this paper utilizes optimization to select optimal
                 features, which makes the detection more precise. As a
                 novelty, a new hybrid algorithm Particle-Assisted Moth
                 Search Algorithm (PA-MSA) is introduced that hybridizes
                 the concept of Moth Search Algorithm (MSA) and Particle
                 Swarm Optimization (PSO), respectively. For the
                 classification process, the optimally chosen features $
                 F_{\rm opt}$ (Fopt) are fed as input, where Deep
                 Convolution Neural Network (DCNN) is used. Finally, a
                 performance-based comparative analysis is conducted
                 among the proposed PA-MSA as well as the existing
                 models with respect to various measures.",
  acknowledgement = ack-nhfb,
  articleno =    "2350056",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Padate:2023:WAO,
  author =       "Roshni Padate and Amit Jain and Mukesh Kalla and And
                 Arvind Sharma",
  title =        "A Widespread Assessment and Open Issues on Image
                 Captioning Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500572",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500572",
  abstract =     "Automated generation of image captions is a demanding
                 AI crisis as it necessitates the exploitation of
                 numerous methods from diverse computer science fields.
                 Deep learning (DL) approaches have revealed marvelous
                 results in a lot of diverse appliances. On the other
                 hand, data augmentation in DL that imitates the
                 quantity and the variety of training data without the
                 need of gathering additional data is a hopeful area in
                 machine learning (ML). Producing textual descriptions
                 for a specified image is a demanding task using the
                 computer. This survey makes a critical analysis of
                 about 65 papers regarding image captioning. More
                 particularly, varied performance measures that are
                 contributed in diverse articles are analyzed. In
                 addition, a comprehensive study is made regarding the
                 maximal performances and varied features deployed in
                 each work. Moreover, chronological analysis and dataset
                 analysis are done and finally, the survey extends with
                 the determination of varied research challenges, which
                 might be productive for the analysts to endorse
                 enhanced upcoming works on image captioning.",
  acknowledgement = ack-nhfb,
  articleno =    "2350057",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Patel:2023:DLE,
  author =       "Miral Jerambhai Patel and Ashish M. Kothari",
  title =        "Deep Learning-Enabled Road Segmentation and
                 Edge-Centerline Extraction from High-Resolution Remote
                 Sensing Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500584",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500584",
  abstract =     "Nowadays, precise and up-to-date maps of road are of
                 great significance in an extensive series of
                 applications. However, it automatically extracts the
                 road surfaces from high-resolution remote sensed images
                 which will remain as a demanding issue owing to the
                 occlusion of buildings, trees, and intricate
                 backgrounds. In order to address these issues, a robust
                 Gradient Descent Sea Lion Optimization-based U-Net
                 (GDSLO-based U-Net) is developed in this research work
                 for road outward extraction from High Resolution (HR)
                 sensing images. The developed GDSLO algorithm is newly
                 devised by the incorporation of Stochastic Gradient
                 Descent (SGD) and Sea Lion Optimization Algorithm
                 (SLnO) algorithm. Input image is pre-processed and
                 U-Net is employed in road segmentation phase for
                 extracting the road surfaces. Meanwhile, training data
                 of U-Net has to be done by using the GDSLO optimization
                 algorithm. Once road segmentation is done, road edge
                 detection and road centerline detection is performed
                 using Fully Convolutional Network (FCN). However, the
                 developed GDSLO-based U-Net method achieved superior
                 performance by containing the estimation criteria,
                 including precision, recall, and F1-measure through
                 highest rate of 0.887, 0.930, and 0.809,
                 respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2350058",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sajitha:2023:AVV,
  author =       "A. S. Sajitha and S. Sridevi Sathya Priya",
  title =        "Analysis of Various Visual Cryptographic Techniques
                 and their Issues Based on Optimization Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500596",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500596",
  abstract =     "Visual Cryptography (VC) is a process employed for the
                 maintenance of secret information by hiding the secret
                 messages that are embedded within the images.
                 Typically, an image is partitioned into a number of
                 shares that are stacked over one another in order to
                 reconstruct back the original image accurately. The
                 major limitation that existed in the traditional VC
                 techniques is pixel expansion, in which pixel expansion
                 is replaced with a number of sub-pixels in individual
                 share, which causes a considerable impact on the
                 contrast and resolution of the image that further
                 gradually decreases the quality of the image. VC is
                 named for its essential characteristics, such as
                 transmitting the images with two or more shares with an
                 equal number of black pixels and color pixel
                 distribution. The secret message can be decrypted using
                 Human Visual System (HVS). In this paper, 50 research
                 papers are reviewed based on various classification
                 algorithms, which are effectively used for the VC
                 technique. The classification algorithms are
                 categorized into three types, namely, meta-heuristic,
                 heuristic, and evolutionary, and the research issues
                 and challenges confronted by the existing techniques
                 are reported in this survey. Moreover, the analysis is
                 done based on the existing research works by
                 considering the classification algorithms, tools, and
                 evaluation metrics.",
  acknowledgement = ack-nhfb,
  articleno =    "2350059",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mustafa:2023:QDD,
  author =       "Adnan A. Mustafa",
  title =        "Quick Dissimilarity Detection for Center-Based Binary
                 Images Via Smart Mapping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500602",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500602",
  abstract =     "In this paper, we present three different smart
                 mapping schemes that improve on the quickness of
                 dissimilarity detection between images. We call the
                 mapping schemes {\em smart\/} because the mapping order
                 is setup intelligently to detect dissimilarity quickly
                 by concentrating its search near the center of the
                 images, which is usually the region of interest in a
                 given scene. Thus, smart mapping is well suited for
                 images when the differences between them are expected
                 to be concentrated near the center of the image. We
                 construct a mapping vector (MV) that contains an
                 ordered list of point mappings which is employed to map
                 points between images in an efficient manner. The focus
                 in this paper is on applying the three different smart
                 mapping schemes to binary images. Furthermore, we test
                 three different mapping densities with each smart
                 mapping scheme and analyze the results. Tests are
                 conducted on two image sets and dissimilarity detection
                 results are compared to results obtained via random
                 mapping, which had been shown to be extremely fast, as
                 predicted by the probabilistic matching model for
                 binary images (PMMBI). We show that by employing smart
                 mapping a great improvement in dissimilarity detection
                 quickness is possible when dissimilarity between images
                 is concentrated near the center of the scene.",
  acknowledgement = ack-nhfb,
  articleno =    "2350060",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sahu:2023:SDR,
  author =       "Geeta Abakash Sahu and Manoj Hudnurkar",
  title =        "Sarcasm Detection: a Review, Synthesis and Future
                 Research Agenda",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823500614",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823500614",
  abstract =     "A literature review on sarcasm detection has been
                 undergone in this research work. To have a meaningful
                 study about the existing works on sarcasm detection, a
                 total of 65 research papers have been analyzed in
                 diverse aspects like the datasets utilized, language,
                 pre-processing technique, type of features, feature
                 extraction technique, machine learning/deep
                 learning-based sarcasm classification. All these papers
                 belong to diverse international as well as national
                 journals. Moreover, the performance of each work in
                 terms of accuracy, {\em F\/} -score and recall will
                 also be manifested. To show the superiority of the
                 works, a comparative evaluation has been undergone in
                 terms of analyzed performances of each of the works.
                 Finally, the works that hold the superior or improved
                 values are furnished. In addition, the current
                 challenges faced by the sarcasm detection system are
                 portrayed, and this will be a milestone for future
                 researchers.",
  acknowledgement = ack-nhfb,
  articleno =    "2350061",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2023:PIR,
  author =       "Yuan Liu",
  title =        "Product Image Recommendation with Transformer Model
                 Using Deep Reinforcement Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467825500202",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467825500202",
  abstract =     "A product image recommendation algorithm with
                 transformer model using deep reinforcement learning is
                 proposed. First, the product image recommendation
                 architecture is designed to collect users' historical
                 product image clicking behaviors through the log
                 information layer. The recommendation strategy layer
                 uses collaborative filtering algorithm to calculate
                 users' long-term shopping interest and gated recurrent
                 unit to calculate users' short-term shopping interest,
                 and predicts users' long-term and short-term interest
                 output based on users' positive and negative feedback
                 sequences. Second, the prediction results are fed into
                 the transformer model for content planning to make the
                 data format more suitable for subsequent content
                 recommendation. Finally, the planning results of the
                 transformer model are input to Deep Q-Leaning Network
                 to obtain product image recommendation sequences under
                 the learning of this network, and the results are
                 transmitted to the data result layer, and finally
                 presented to users through the presentation layer. The
                 results show that the recommendation results of the
                 proposed algorithm are consistent with the user's
                 browsing records. The average accuracy of product image
                 recommendation is 97.1\%, the maximum recommended time
                 is 1.0$s$ the coverage and satisfaction are high, and
                 the practical application effect is good. It can
                 recommend more suitable products for users and promote
                 the further development of e-commerce.",
  acknowledgement = ack-nhfb,
  articleno =    "2550020",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Anonymous:2023:AIV,
  author =       "Anonymous",
  title =        "Author Index (Volume 23)",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "23",
  number =       "06",
  pages =        "??--??",
  month =        nov,
  year =         "2023",
  DOI =          "https://doi.org/10.1142/S0219467823990012",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467823990012",
  acknowledgement = ack-nhfb,
  articleno =    "2399001",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kamble:2024:VUM,
  author =       "Tanaji Umaji Kamble and Shrinivas Padmakar Mahajan",
  title =        "{$3$D} Vision Using Multiple Structured Light-Based
                 {Kinect} Depth Cameras",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467824500013",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500013",
  abstract =     "Real-time 3D scanning of a scene or object using
                 multiple depth cameras is often required in many
                 applications but is still a challenging task for the
                 computer vision community, especially when the object
                 or scene is partially occluded and dynamic. If active
                 depth sensors are used in this case, their resulting
                 depth map quality gets degraded due to interference
                 between active radiations from each depth sensor.
                 Passive 3D sensors like stereo cameras can avoid the
                 issue of interference as they do not emit any active
                 radiation, but they face correspondence problems. Since
                 releasing the commodity depth sensor Microsoft Kinect,
                 researchers are getting more interested in active
                 depth-sensing. However, Kinect sensors have some easily
                 noticeable limitations concerning 3D reconstruction
                 such as: they can provide depth maps for a limited
                 range, their field of view is restricted and holes are
                 observed in the depth map due to occlusion. The
                 above-mentioned limitations can be overcome if multiple
                 Kinect sensors are used simultaneously instead of a
                 single Kinect sensor. Still, the challenge here is to
                 avoid interference between these sensors. We present a
                 comprehensive review of possible solutions to avoid
                 interference between multiple Kinect sensors.
                 Furthermore, we introduce the Kinect technology in
                 detail along with applications where multiple Kinect
                 sensors are used in the literature. We expect that this
                 paper will be helpful to the researchers who want to
                 use multiple Kinect sensors in sharing the workplace in
                 their research.",
  acknowledgement = ack-nhfb,
  articleno =    "2450001",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Deepak:2024:ASI,
  author =       "A. V. S. Deepak and Umesh Ghanekhar",
  title =        "Analysis of Single Image Super-Resolution Techniques:
                 an Evolutionary Study",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500025",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500025",
  abstract =     "Single image super-resolution (SR) is a technique that
                 reconstructs a high-resolution (HR) image from a single
                 low-resolution (LR) input image. The main objective of
                 super-resolution algorithms is to achieve a
                 high-resolution image that is consistent with the input
                 low-resolution image but has enhanced spectral
                 properties. In this review, several research papers and
                 their corresponding algorithms have been reviewed and
                 are classified based on their methodology. The
                 principal objective of this review is to understand the
                 evolution of SISR techniques from basic interpolation
                 techniques to sophisticated convolutional neural
                 networks. This article also presents design
                 considerations for future advancements.",
  acknowledgement = ack-nhfb,
  articleno =    "2450002",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Roy:2024:SRM,
  author =       "Srinjoy Roy and D. Binu and B. R. Rajakumar and
                 Vamsidhar Talasila and And Abhishek Bhatt",
  title =        "Super Resolved Maize Plant Leaves Disease Detection
                 Using Optimal Generative Adversarial Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500037",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500037",
  abstract =     "Agriculture plays a vital role in the economy and crop
                 disease causes huge financial losses every year. The
                 losses can be reduced by detecting the disease
                 accurately. The variation in light intensity and
                 complex background of the agricultural field in
                 detecting the maize leaves disease are the biggest
                 challenges. An optimization algorithm, named Cat Swarm
                 Political Optimizer Algorithm (CSPOA) has been
                 developed in this research to detect the disease of a
                 maize plant leaf. Our proposed algorithm is an
                 integration of the Cat Swarm Optimization (CSO) and
                 Political Optimizer (PO) algorithm. Anisotropic
                 filtering performs pre-processing for removing noise
                 and the Region of Interest (ROI) extraction for
                 enhancing the image quality. The super resolution image
                 is obtained from the Low Resolution (LR) images using
                 kernel regression model. After obtaining the super
                 resolution image, the salient map extraction has been
                 carried out for representing the saliency. Finally, the
                 maize plant leaves disease classification process is
                 done using General Adversarial Network (GAN) for
                 identifying the maize leaves disease. The training of
                 GAN develops the CSPOA. On comparing with the existing
                 maize plant leaves disease detection approaches, the
                 developed CSPOA-based GAN performed with a maximum
                 accuracy 0.9056, maximum sensitivity 0.9599, and the
                 maximum specificity 0.9592, respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2450003",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Naik:2024:NSE,
  author =       "Manoj Kumar Naik and Monorama Swain and Rutuparna
                 Panda and And Ajith Abraham",
  title =        "Novel Square Error Minimization-Based Multilevel
                 Thresholding Method for {COVID-19} {X}-Ray Image
                 Analysis Using Fast Cuckoo Search",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500049",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500049",
  abstract =     "Coronavirus outbreaks in 2019 (COVID-19) have been a
                 huge disaster in the fields of health, economics,
                 education, and tourism in the last two years. For
                 diagnosis, a quick interpretation of the COVID-19 chest
                 X-ray image is required. There is also a strong need to
                 find an efficient multiclass segmentation technique for
                 the analysis of COVID-19 X-ray images. Most of the
                 threshold selection techniques are entropy-based.
                 Nevertheless, these techniques suffer from their
                 dependencies on the spatial distribution of grey
                 values. To tackle these issues, a novel non-entropic
                 threshold selection method is proposed, which is the
                 primary key contribution having found a new source of
                 information to the biomedical image processing field.
                 The firsthand Square Error (SE)-based objective
                 function is suggested. The second key contribution is
                 the new optimizer called Fast Cuckoo Search (FCS),
                 which is useful and brings novel ideas into the
                 subject, used to optimize the suggested objective
                 functions for computing the optimal thresholds. To
                 ensure a faster convergence with a quality optimal
                 solution, we include extra exploitation together with a
                 chance factor. The FCS is validated using the
                 well-known classical and CEC 2014 benchmark test
                 functions, which shows a significant improvement over
                 its predecessors --- Adaptive Cuckoo Search (ACS) and
                 other state-of-the-art optimizers. Further, the SE
                 minimization-based optimal multilevel thresholding
                 method using the FCS, coined as SE-FCS, is proposed. To
                 experiment, images are considered from the Kaggle
                 Radiography database. We have compared its performances
                 with Tsallis, Kapur's, and Masi entropy-based
                 techniques using well-known segmentation metrics and
                 achieved a performance increase of 2.95\%, 5.51\% and
                 10.50\%, respectively. The proposed method shows
                 superiority using Friedman's mean rank statistical test
                 and ranked first.",
  acknowledgement = ack-nhfb,
  articleno =    "2450004",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lyu:2024:AIO,
  author =       "Chengang Lyu and Mengqi Zhang and And Jie Jin",
  title =        "An Adaptive Illumination Optimization Method for Local
                 Overexposed Image",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500050",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500050",
  abstract =     "In order to solve the local overexposure caused by
                 uneven surface reflectance, this paper proposes a
                 fast-adaptive illumination control method with a
                 camera-projector system. At first, an image is captured
                 by the camera and the local overexposed area is
                 segmented using saliency detection. Then the calculated
                 image is projected onto the object by the projector as
                 corrective illumination. The calculation process
                 includes the inversion of the gray value in the
                 overexposed area and the adjustment based on the
                 position and depth information of the object. The
                 high-exposure saturated regional which affects the
                 target recognition is thus reduced, and the original
                 illumination intensity is reserved for the other
                 regions. This process is iterated until the optimal
                 illumination is achieved. The resulting image for each
                 iteration is evaluated using Blind/no Reference Image
                 Space Quality Estimator (BRISQUE). When BRISQUE value
                 reaches the minimum, a high-quality image is achieved.
                 The experiments show that the proposed approach can
                 significantly improve the speed of obtaining normally
                 exposed images, and this system provides new ideas for
                 industry image acquisition.",
  acknowledgement = ack-nhfb,
  articleno =    "2450005",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Elloumi:2024:FRO,
  author =       "Nessrine Elloumi and Habiba Loukil and And Med Salim
                 Bouhlel",
  title =        "Full-Reference Objective Quality Metric for
                 Three-Dimensional Deformed Models",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500062",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500062",
  abstract =     "Three-dimensional data are generally represented by
                 triangular meshes. The 3D data are used in several
                 fields including remote 3D games, 3D medical
                 application, 3D virtual worlds and 3D augmented reality
                 application. These applications require displaying,
                 printing or exchanging the 3D models through the
                 network to optimize the rendering of the 3D models and
                 3D applications, which include different treatments,
                 for example, smoothing, compression, re-meshing,
                 simplification, watermarking, etc. However, these
                 processes generate distortions that affect the quality
                 of the rendered 3D data. Thus, subjective or objective
                 metrics are required for assessing the visual quality
                 of the deformed models to evaluate the efficiency of
                 the applied algorithms. In this context, we introduce a
                 new perceptual full-reference metric that compare two
                 3D meshes based on their 3D content information. The
                 proposed metric integrates the relativity and
                 selectivity properties of the Human visual system (HVS)
                 independent of the mesh type and connectivity (e.g.
                 Triangular, Quadrilateral, Tetrahedron, Hexahedron),
                 which represent a limit in the existing method, in
                 order to capture the perceptual quantity of the
                 distortion by the observer. The results of the proposed
                 approach outperform the existing metrics and have a
                 high correlation with the subjective measures. We use
                 the two correlation coefficients Spearman Rank (Rs) and
                 Pearson Rank (Rp) in order to assess the performance of
                 the proposed metric.",
  acknowledgement = ack-nhfb,
  articleno =    "2450006",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pedalanka:2024:EDN,
  author =       "P. S. Subhashini Pedalanka and Manchikalapudi Satya
                 Sai Ram and And Duggirala Sreenivasa Rao",
  title =        "An Enhanced Deep Neural Network-Based Approach for
                 Speaker Recognition Using Triumvirate Euphemism
                 Strategy",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500074",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500074",
  abstract =     "Automatic Speech Recognition (ASR) has been an
                 intensive research area during the recent years in
                 internet to enable natural human--machine
                 communication. However, the existing Deep Neutral
                 Network (DNN) techniques need more focus on feature
                 extraction process and recognition accuracy. Thus, an
                 enhanced deep neural network (DNN)-based approach for
                 speaker recognition with a novel Triumvirate Euphemism
                 Strategy (TES) is proposed. This overcomes poor feature
                 extraction from Mel-Frequency Cepstral Coefficient
                 (MFCC) map by extracting the features based on petite,
                 hefty and artistry of the features. Then, the features
                 are trained with Silhouette Martyrs Method (SMM)
                 without any inter-class and intra-class separability
                 problems and margins are affixed between classes with
                 three new loss functions, namely A-Loss, AM-Loss and
                 AAM-Loss. Additionally, the parallelization is done by
                 a mini-batch-based BP algorithm in DNN. A novel
                 Frenzied Heap Atrophy (FHA) with a multi-GPU model is
                 introduced in addition with DNN to enhance the
                 parallelized computing that accelerates the training
                 procedures. Thus, the outcome of the proposed technique
                 is highly efficient that provides feasible extraction
                 features and gives incredibly precise results with
                 97.5\% accuracy in the recognition of speakers.
                 Moreover, various parameters were discussed to prove
                 the efficiency of the system and also the proposed
                 method outperformed the existing methods in all
                 aspects.",
  acknowledgement = ack-nhfb,
  articleno =    "2450007",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2024:OAC,
  author =       "V. Rajesh Kumar and P. Aruna Jeyanthy and And R.
                 Kesavamoorthy",
  title =        "Optimization-Assisted {CNN} Model for Fault
                 Classification and Site Location in Transmission
                 Lines",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500086",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500086",
  abstract =     "The theme of the paper is to emphasize the detection
                 and classification of faults and their site location in
                 the transmission line using machine learning techniques
                 which help to indemnify the foul-up of the humans in
                 identifying the site and type of occurrence of fault.
                 Moreover, the transient stability is a supreme one in
                 power systems and so the disturbances like faults are
                 required to be separated to preserve the transient
                 stability. In general, the protection of the
                 transmission line includes the installation of relays
                 at both ends of the line that constantly monitor
                 voltages and currents and operate unless a fault occurs
                 on a line. Therefore, this paper intends to introduce a
                 novel transmission line protection model by exploiting
                 the hybrid optimization concept to train the
                 Convolutional Neural Network (CNN). Here, the fault
                 detection, classification and site location are
                 diagnosed by using CNN which is trained and tested by
                 making use of diverse synthetic field data derived from
                 the simulation models of distinct types of transmission
                 lines. Hence, the location and the type of faults will
                 be predicted by the CNN depending on the fault signal
                 characteristics which are optimally trained by a new
                 hybrid algorithm named Chicken Swarm Insisted Spotted
                 Hyena (CSI-SH) Algorithm that hybrids both the concept
                 of Spotted Hyena Optimization (SHO) and Chicken Swarm
                 Optimization (CSO). Finally, the proposed method based
                 on CNN for fault classification and site location of
                 transmission lines is implemented in MATLAB/Simulink
                 and the performances are compared with various measures
                 like classification accuracy, fault detection rate and
                 so on.",
  acknowledgement = ack-nhfb,
  articleno =    "2450008",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bapatla:2024:DHO,
  author =       "Sesikala Bapatla and J. Harikiran",
  title =        "Deer Hunting Optimization with {$3$D}-Convolutional
                 Neural Network for Diabetic Retinopathy Classification
                 Model",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500098",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500098",
  abstract =     "A retina disease caused by high glucose levels in the
                 blood is called Diabetic Retinopathy (DR) and is the
                 world's leading cause of blindness. To avoid or delay
                 vision degradation and loss, early diagnosis and
                 treatment are required. As a result, the creation of an
                 automated method for accurate DR identification is
                 essential. For this, in this paper, a 3D-Convolution
                 Neural Network (3D-CNN) with Deer Hunting Optimization
                 (DHO) algorithm is proposed for detecting and
                 classifying DR images. The proposed 3D-CNN-DHO approach
                 includes four phases such as pre-processing,
                 segmentation, feature extraction, and classification.
                 The contrast of the DR image is first improved using a
                 Contrast-Limited Adaptive Histogram Equalization
                 (CLAHE) approach. Subsequently, the threshold-based
                 effective segmentation is carried out. Then, the
                 Resnet50 model is implemented to extract the features
                 from the image. Finally, 3D-CNN-DHO-based classifier
                 model is implemented to categorize the various DR
                 stages. The experiments are carried out in detail and
                 evaluated on the Messidor DR benchmark dataset. The
                 acquired experimental result demonstrated the
                 3D-CNN-DHO model's outstanding qualities by achieving
                 optimal specificity, sensitivity, recall, precision,
                 and accuracy.",
  acknowledgement = ack-nhfb,
  articleno =    "2450009",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Gadde:2024:CMD,
  author =       "Swetha Gadde and J. Amutharaj and And S. Usha",
  title =        "Cloud Multimedia Data Security by
                 Optimization-Assisted Cryptographic Technique",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500104",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500104",
  abstract =     "Currently, the size of multimedia data is rising
                 gradually from gigabytes to petabytes, due to the
                 progression of a larger quantity of realistic data. The
                 majority of big data is conveyed via the internet and
                 they were accumulated on cloud servers. Since cloud
                 computing offers internet-oriented services, there were
                 a lot of attackers and malevolent users. They always
                 attempt to deploy the private data of users without any
                 right access. At certain times, they substitute the
                 real data by any counterfeit data. As a result, data
                 protection has turned out to be a noteworthy concern in
                 recent times. This paper aims to establish an
                 optimization-based privacy preservation model for
                 preserving multimedia data by selecting the optimal
                 secret key. Here, the encryption and decryption process
                 is carried out by Improved Blowfish cryptographic
                 technique, where the sensitive data in cloud server is
                 preserved using the optimal key. Optimal key generation
                 is the significant procedure to ensure the objectives
                 of integrity and confidentiality. Likewise, data
                 restoration is the inverse process of sanitization
                 (decryption). In both the cases, key generation remains
                 a major aspect, which is optimally chosen by a novel
                 hybrid algorithm termed as ``Clan based Crow Search
                 with Adaptive Awareness probability (CCS-AAP). Finally,
                 an analysis is carried out to validate the improvement
                 of the proposed method.",
  acknowledgement = ack-nhfb,
  articleno =    "2450010",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Yang:2024:DDS,
  author =       "Hailong Yang and Yinghao Liu and And Tian Xia",
  title =        "Defect Detection Scheme of Pins for Aviation
                 Connectors Based on Image Segmentation and Improved
                 {RESNET-50}",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500116",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500116",
  abstract =     "In this paper, a new detection method of pin defects
                 based on image segmentation and ResNe-50 is proposed,
                 which realizes the defect detection of faulty pins in
                 many aviation connectors. In this paper, a new dataset
                 image segmentation method is used to segment many
                 aviation connectors in a single image to generate a
                 dataset, which reduces the tedious work of manually
                 labeling the dataset. In the defect detection model,
                 based on ResNet-50, a ResNet-B residual structure is
                 introduced to reduce the loss of features during
                 information extraction; a continuously differentiable
                 CELU is used as the activation function to reduce the
                 neuron death problem of ReLU; a new deformable
                 convolution network (DCN v2) is introduced as the
                 convolution kernel structure of the model to improve
                 the recognition of aviation connectors with prominent
                 geometric deformation pin recognition. The improved
                 model achieved 97.2\% and 94.4\% accuracy for skewed
                 and missing pins, respectively, in the experiments. The
                 detection accuracy improved by 1.91\% to 96.62\%
                 compared to the conventional ResNet-50. Compared with
                 the traditional model, the improved model has better
                 generalization ability.",
  acknowledgement = ack-nhfb,
  articleno =    "2450011",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2024:MLA,
  author =       "K. Antony Kumar and M. J. Carmel Mary Belinda",
  title =        "A Multi-Layer Acoustic Neural Network-Based
                 Intelligent Early Diagnosis System for Rheumatic Heart
                 Disease",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500128",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500128",
  abstract =     "Rheumatic Heart Disease (RHD) is a disorder of heart
                 caused by streptococcal throat infection followed by
                 the organ damage, irreversible valve damage and heart
                 failure. Acute Rheumatic Fever (ARF) is a precursor to
                 the disease. Sometimes, RHD can occur without any signs
                 or symptoms, and if there are any symptoms, they occur
                 with the infection in the heart valves and fever. Due
                 to these issues, respiratory problems occur with chest
                 pain and tremors. Additionally, the symptoms include
                 faint, heart murmurs, stroke and unexpected collapse.
                 The techniques available try to detect the RHD as early
                 as possible. Although the recent medical health care
                 department uses crucial techniques, they are not
                 accurate in terms of symptom classification, precision
                 and prediction. On the scope, we are developing
                 Multi-Layered Acoustic Neural (MLAN) Networks to detect
                 the RHD symptoms using heart beat sound and
                 Electrocardiogram (ECG) measurements. In this proposed
                 MLAN system, the novel techniques such as
                 multi-attribute acoustic data sampling model, heart
                 sound sampling procedures, ECG data sampling model, RHD
                 Recurrent Convolutional Network (RRCN) and Acoustic
                 Support Vector Machine (ASVM) are used for increasing
                 the accuracy. In the implementation section, the
                 proposed model has been compared to the Long Short-Term
                 Memory-based Cardio (LSTC) data analysis model,
                 Cardio-Net and Video-Based Deep Learning (VBDL)
                 techniques. In this comparison, the proposed system has
                 10\%--17\% higher accuracy in RHD detection than
                 existing techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "2450012",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Duan:2024:ABR,
  author =       "Xueying Duan",
  title =        "Abnormal Behavior Recognition for Human Motion Based
                 on Improved Deep Reinforcement Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "01",
  pages =        "??--??",
  month =        jan,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467825500299",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Thu May 23 07:14:57 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467825500299",
  abstract =     "Recognizing abnormal behavior recognition (ABR) is an
                 important part of social security work. To ensure
                 social harmony and stability, it is of great
                 significance to study the identification methods of
                 abnormal human motion behavior. Aiming at the low
                 accuracy of human motion ABR method, ABR method for
                 human motion based on improved deep reinforcement
                 learning (DRL) is proposed. First, the background image
                 is processed in combination with the Gaussian model;
                 second, the background features and human motion
                 trajectory features are extracted, respectively;
                 finally, the improved DRL model is constructed, and the
                 feature information is input into the improvement model
                 to further extract the abnormal behavior features, and
                 the ABR of human motion is realized through the
                 interaction between the agent and the environment. The
                 different methods were examined based on UCF101 data
                 set and HiEve data set. The results show that the
                 accuracy of human motion key point acquisition and
                 posture estimation accuracy is high, the proposed
                 method sensitivity is good, and the recognition
                 accuracy of human motion abnormal behavior is as high
                 as 95.5\%. It can realize the ABR for human motion and
                 lay a foundation for the further development of
                 follow-up social security management.",
  acknowledgement = ack-nhfb,
  articleno =    "2550029",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Rao:2024:HSE,
  author =       "K. Venkateswara Rao and B. Venkata Ramana Reddy",
  title =        "{HM-SMF}: an Efficient Strategy Optimization using a
                 Hybrid Machine Learning Model for Stock Market
                 Prediction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S021946782450013X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782450013X",
  abstract =     "Stock market forecasting is a significant task, and
                 investing in the stock marketplace is a significant
                 part of monetary research due to its high risk.
                 Therefore, accurate forecasting of stock market
                 analysis is still a challenge. Due to stable and
                 volatile data, stock market forecasting remains a major
                 challenge for investors. Recent machine learning (ML)
                 models have been able to reduce the risk of stock
                 market forecasting. However, diversity remains a key
                 challenge in developing better erudition models and
                 extracts more intellectually priceless qualities to
                 auxiliary advanced predictability. In this paper, we
                 propose an efficient strategy optimization using a
                 hybrid ML model for stock market prediction (HM-SMP).
                 The first contribution of the proposed HM-SMP model is
                 to introduce chaos-enhanced firefly bowerbird
                 optimization (CEFBO) algorithm for optimal feature
                 selection among multiple features which reduce the data
                 dimensionality. Second, we develop a hybrid
                 multi-objective capuchin with a recurrent neural
                 network (HC-RNN) for the prediction of the stock market
                 which enhances the prediction accuracy. We use
                 supervised RNN to predict the closing price. Finally,
                 to estimate the presence of the proposed HM-SMP model
                 through the benchmark, stock market datasets and the
                 performance can be compared with the existing
                 state-of-the-art models in terms of accuracy,
                 precision, recall, and $F$-measure.",
  acknowledgement = ack-nhfb,
  articleno =    "2450013",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Moradi:2024:IIE,
  author =       "Saed Moradi and Jahed Moradi and Saeid Aghaziyarati
                 and And Hadi Shahraki",
  title =        "Infrared Image Enhancement Based on Optimally Weighted
                 Multi-Scale {Laplacian} of {Gaussian} and Local
                 Statistics Using Particle Swarm Optimization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500141",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500141",
  abstract =     "Infrared imagery is extensively used in defense,
                 remote sensing and medical applications. While the
                 infrared images have many advantages over RGB images,
                 the details in these images are usually blurred which
                 in turn leads to some difficulties for human operators.
                 In this paper, a new method based on Laplacian of
                 Gaussian scale-space and local variance is presented to
                 improve the visual quality of the infrared images. At
                 the first step, the Gaussian scale-space is constructed
                 by convolving the original image with different
                 Gaussian kernels. Then, the two-dimensional Laplacian
                 kernels are convolved with the Gaussian scale-space to
                 achieve details with both positive as well as negative
                 contrasts. The weighted details are added to the
                 original image to deblur the dim areas. At the final
                 step, to increase the dynamic range of the image and
                 have better visual quality, the local variance of the
                 image is also added to the output of the previous step.
                 Since finding optimum weighting coefficients is a
                 difficult task empirically, here, we use a
                 population-based meta-heuristic optimization algorithm
                 called particle swarm optimization (PSO) to find the
                 optimum values for weighting coefficient values. Beside
                 qualitative comparison, Structural Similarity (SSIM)
                 and second-derivative-like measure of enhancement
                 (SDME) are used to quantitatively investigate the
                 images quality. The proposed method outperforms the
                 baseline algorithms in both qualitative and
                 quantitative perspectives.",
  acknowledgement = ack-nhfb,
  articleno =    "2450014",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Thakkar:2024:EBP,
  author =       "Priyanka Bibay Thakkar and R. H. Talwekar",
  title =        "An Efficient Blood Pressure Estimation and Risk
                 Analysis System of {PPG} Signals Using {IDA} and
                 {MPPIW-DLNN} Algorithms",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500153",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500153",
  abstract =     "The non-invasive Blood Pressure Estimation (BPE)
                 utilizing the technology of photoplethysmography (PPG)
                 gains significant interest because PPG could be
                 extensively employed to wearable sensors. Here, a
                 method for estimating Systolic Blood pressure (SBP), as
                 well as Diastolic Blood pressure (DBP), grounded only
                 on a PPG signal utilizing the Image Denoising
                 Algorithms (IDA) algorithms is proposed. Also, a
                 classification methodology to execute the risk analysis
                 (RA) of the BP patients utilizing Moore--Penrose
                 Pseudo-Inverse Matrix-Deep Learning Neural Network
                 (MPPIW-DLNN) is proposed. The preprocessing is then
                 done on the input PPG signal utilizing the
                 Modified--Chebyshev Filter (CF) to eradicate the
                 unwanted information existent in the signal. Afterward,
                 the BPE is done utilizing IDA, which categorizes those
                 components into (i) SBP and (ii) DBP. The MPPIW-DLNN
                 provides the results of four sorts of risk classes like
                 (i) stroke, (ii) heart failure (HF), (iii) heart attack
                 (HA), and (iv) aneurysm identified from the inputted
                 PPG signal.",
  acknowledgement = ack-nhfb,
  articleno =    "2450015",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumawat:2024:TVS,
  author =       "Manisha Kumawat and Arti Khaparde",
  title =        "Time-Variant Satellite Vegetation Classification
                 Enabled by Hybrid Metaheuristic-Based Adaptive
                 Time-Weighted Dynamic Time Warping",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500165",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500165",
  abstract =     "Land cover data is very significant for designing the
                 earth system, managing the natural resources, and also
                 for performing conservation planning. Time-series data
                 are captured with their dynamic vegetation behavior
                 using remote sensing technology, which is broadly
                 utilized in land cover mapping. Most of the Vegetation
                 Index (VI) such as the Enhanced Vegetation Index (EVI)
                 and Normalized Difference Vegetation Index (NDVI)
                 comprises commonly employed features that are obtained
                 from the time-series spectral data. But, these VIs are
                 not validated as the optimal techniques for generating
                 the temporal profiles. Recent researches highly depend
                 on optical satellite imagery for performing these
                 above-mentioned tasks. Dynamic Time Warping (DTW) is
                 said to be an effective optimal solution for solving
                 the existing challenges, especially the improved
                 version of DTW named Time-Weighted Dynamic Time Warping
                 (TWDTW) is used for time-series analysis regarding the
                 time-series vegetation classification. Yet, the TWDTW
                 efficiency is not shown with other comparative machine
                 learning approaches owing to the classification of
                 vegetation type in the mountain areas. The major goal
                 of this paper is to research and create a novel
                 approach for distinguishing the kind of vegetation in a
                 farm region near Ujani Dam in Solapur District,
                 Maharashtra using time-series analysis. For time-series
                 analysis employing satellite images, the suggested
                 model offers a unique Adaptive Time-Weighted Dynamic
                 Time Warping (ATWDTW). The farm's satellite images are
                 first pre-processed before being sent to ATWDTW for
                 examination. The TWDTW idea is optimized for
                 classification performance using a new hybrid
                 metaheuristic technique named Adaptive Coyote Crow
                 Search Optimization (ACCSO). From the experimental
                 results, the performance of the suggested ACCSO-ATWDTW
                 correspondingly provides superior performance to the
                 traditional approaches, where the designed model using
                 ACCSO-ATWDTW provides 7.2\%, 5.2\%, 9.9\%, 4.55\%, and
                 2.33\% higher MCC than the MFO-ATWDTW, BSA-ATWDTW,
                 MF-BSA-ATWDTW, CSA-ATWDTW, and COA-ATWDTW at the
                 maximum iteration of 200. This proved the robustness
                 and less sensitivity to training samples of the TWDTW
                 method when applied to mountain vegetation-type
                 classifications.",
  acknowledgement = ack-nhfb,
  articleno =    "2450016",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sowmya:2024:CNB,
  author =       "M. N. Sowmya and Keshava Prasanna",
  title =        "Convoluted Neighborhood-Based Ordered-Dither Block
                 Truncation Coding for Ear Image Retrieval",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500177",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500177",
  abstract =     "Image retrieval is a significant and hot research
                 topic among researchers that drives the focus of
                 researchers from keyword toward semantic-based image
                 reconstruction. Nevertheless, existing image retrieval
                 investigations still have a shortage of significant
                 semantic image definition and user behavior
                 consideration. Hence, there is a necessity to offer a
                 high level of assistance towards regulating the
                 semantic gap between low-level visual patterns and
                 high-level ideas for a better understanding between
                 humans and machines. Hence, this research devises an
                 effective medical image retrieval strategy using
                 convoluted neighborhood-based Ordered-dither block
                 truncation coding (ODBTC). The developed approach is
                 devised by modifying the ODBTC concept using a
                 convoluted neighborhood mechanism. Here, the convoluted
                 neighborhood-based color co-occurrence feature (CCF)
                 and convoluted neighborhood-based bit pattern feature
                 (BBF) are extracted. Finally, cross-indexing is
                 performed to convert the feature points into binary
                 codes for effective image retrieval. Meanwhile, the
                 proposed convoluted neighborhood-based ODBTC has
                 achieved maximum precision, recall, and f-measure with
                 values of 0.740, 0.680, and 0.709.",
  acknowledgement = ack-nhfb,
  articleno =    "2450017",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chaurasiya:2024:RRV,
  author =       "Rashmi Chaurasiya and Dinesh Ganotra",
  title =        "Reflection Removal with Varied Field of View Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500189",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500189",
  abstract =     "Due to the presence of an additional glass pane
                 between the camera and the scene, an additional
                 reflection scene is captured in the image apart from
                 the desired object sometimes. Images are more often
                 captured from mobile handsets these days which have
                 multiple cameras. This paper gives the advantage of
                 multiple cameras. There exists a disparity and varied
                 field of view when images are captured with multiple
                 cameras. We use these two factors to act as a cue to
                 remove reflection, as reflection intensity across the
                 image pairs change with different field-of-view. The
                 proposed method is robust and convenient to implement
                 as it does not require an additional hardware, for
                 example, light field camera for stereo images. Also, it
                 does not make assumptions about the appearance or
                 intensity of reflection.",
  acknowledgement = ack-nhfb,
  articleno =    "2450018",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shaikh:2024:RTM,
  author =       "Shakil A. Shaikh and Jayant J. Chopade and And Mohini
                 Pramod Sardey",
  title =        "Real-Time Multi-Object Detection Using Enhanced
                 {Yolov5-7S} on Multi-{GPU} for High-Resolution Video",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500190",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500190",
  abstract =     "Multiple objects tracking in a video sequence can be
                 performed by detecting and distinguishing the objects
                 that appear in the sequence. In the context of computer
                 vision, the robust multi-object tracking problem is a
                 difficult problem to solve. Visual tracking of multiple
                 objects is a vital part of an autonomous driving
                 vehicle's vision technology. Wide-area video
                 surveillance is increasingly using advanced imaging
                 devices with increased megapixel resolution and
                 increased frame rates. As a result, there is a huge
                 increase in demand for high-performance computation
                 system of video surveillance systems for real-time
                 processing of high-resolution videos. As a result, in
                 this paper, we used a single stage framework to solve
                 the MOT problem. We proposed a novel architecture in
                 this paper that allows for the efficient use of one and
                 multiple GPUs are used to process Full High Definition
                 video in real time. For high-resolution video and
                 images, the suggested approach is real-time
                 multi-object detection based on Enhanced Yolov5-7S on
                 Multi-GPU Vertex. We added one more layer at the top in
                 backbone to increase the resolution of feature
                 extracted image to detect small object and increase the
                 accuracy of model. In terms of speed and accuracy, our
                 proposed approach outperforms the state-of-the-art
                 techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "2450019",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Du:2024:SSS,
  author =       "Fuhe Du and Bo Peng and Zaid Al-huda and And Jing
                 Yao",
  title =        "Semi-Supervised Skin Lesion Segmentation via Iterative
                 Mask Optimization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500207",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500207",
  abstract =     "Deep learning-based skin lesion segmentation methods
                 have achieved promising results in the community.
                 However, they are usually based on fully supervised
                 learning and require many high-quality ground truths.
                 Labeling the ground truths takes a lot of labor,
                 material, and financial resources. We propose a novel
                 semi-supervised skin lesion segmentation method to
                 solve this problem. First, a hierarchical image
                 segmentation algorithm is used to generate optimal
                 segmentation maps. Then, fully supervised training is
                 performed on a small part of the images with ground
                 truths. The resulting pseudo masks are generated to
                 train the rest of the images. The optimal segmentation
                 maps are utilized in this process to refine the pseudo
                 masks. Experiments show that the proposed method can
                 improve the performance of semi-supervised learning for
                 skin lesion segmentation by reducing the gap with fully
                 supervised learning methods. Moreover, it can reduce
                 the workload of labeling the ground truths. Extensive
                 experiments are conducted on the open dataset to
                 validate the efficiency of the proposed method. The
                 results show that our method is competitive in
                 improving the quality of semi-supervised
                 segmentation.",
  acknowledgement = ack-nhfb,
  articleno =    "2450020",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mulla:2024:WGE,
  author =       "Samina Mulla and Nuzhat F. Shaikh",
  title =        "Weighted Graph Embedding Feature with Bi-Directional
                 Long Short-Term Memory Classifier for Multi-Document
                 Text Summarization",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500220",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500220",
  abstract =     "In this digital era, there is a tremendous increase in
                 the volume of data, which adds difficulties to the
                 person who utilizes particular applications, such as
                 websites, email, and news. Text summarization targets
                 to reduce the complexity of obtaining statistics from
                 the websites as it compresses the textual document to a
                 short summary without affecting the relevant
                 information. The crucial step in multi-document
                 summarization is obtaining a relationship between the
                 cross-sentence. However, the conventional methods fail
                 to determine the inter-sentence relationship,
                 especially in long documents. This research develops a
                 graph-based neural network to attain an inter-sentence
                 relationship. The significant step in the proposed
                 multi-document text summarization model is forming the
                 weighted graph embedding features. Furthermore, the
                 weighted graph embedding features are utilized to
                 evaluate the relationship between the document's words
                 and sentences. Finally, the bidirectional long
                 short-term memory (BiLSTM) classifier is utilized to
                 summarize the multi-document text summarization. The
                 experimental analysis uses the three standard datasets,
                 the Daily Mail dataset, Document Understanding
                 Conference (DUC) 2002, and Document Understanding
                 Conference (DUC) 2004 dataset. The experimental outcome
                 demonstrates that the proposed weighted graph embedding
                 feature + BiLSTM model exceeds all the conventional
                 methods with Precision, Recall, and F1 score of 0.5352,
                 0.6296, and 0.5429, respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2450022",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Enturi:2024:ODC,
  author =       "B. Krishna Manash Enturi and A. Suhasini and And
                 Narayana Satyala",
  title =        "Optimized Deep {CNN} with Deviation Relevance-based
                 {LBP} for Skin Cancer Detection: Hybrid Metaheuristic
                 Enabled Feature Selection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500232",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500232",
  abstract =     "Segmentation of skin lesions is a significant and
                 demanding task in dermoscopy images. This paper
                 proposes a new skin cancer recognition scheme, with:
                 ``Pre-processing, Segmentation, Feature extraction,
                 Optimal Feature Selection and Classification''. Here,
                 pre-processing is done with certain processes. The
                 pre-processed images are segmented via the ``Otsu
                 Thresholding model''. The third phase is feature
                 extraction, where Deviation Relevance-based ``Local
                 Binary Pattern (DRLBP), Gray-Level Co-Occurrence Matrix
                 (GLCM) features and Gray Level Run-Length Matrix (GLRM)
                 features'' are extracted. From these extracted
                 features, the optimal features are chosen via Particle
                 Updated WOA (PU-WOA) model. Subsequently,
                 classification occurs via Optimized DCNN and NN to
                 classify the skin lesion. To make the classification
                 more precise, the DCNN is optimized by the introduced
                 algorithm. The result has shown a higher accuracy of
                 0.998737, when compared with other extant models like
                 IPSO, IWOA, PSO+CNN, WOA+CNN and CNN schemes.",
  acknowledgement = ack-nhfb,
  articleno =    "2450023",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Pakhare:2024:HML,
  author =       "Jayamala D. Pakhare and Mahadev D. Uplane",
  title =        "Hybrid Mayfly {L{\'e}vy} Flight Distribution
                 Optimization Algorithm-Tuned Deep Convolutional Neural
                 Network for Indoor--Outdoor Image Classification",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500244",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500244",
  abstract =     "Image classification in the image is the persistent
                 task to be computed in robotics, automobiles, and
                 machine vision for sustainability. Scene categorization
                 remains one of the challenging parts of various
                 multi-media technologies implied in human--computer
                 communication, robotic navigation, video surveillance,
                 medical diagnosing, tourist guidance, and drone
                 targeting. In this research, a Hybrid Mayfly L{\'e}vy
                 flight distribution (MLFD) optimization algorithm-tuned
                 deep convolutional neural network is proposed to
                 effectively classify the image. The feature extraction
                 process is a significant task to be executed as it
                 enhances the classifier performance by reducing the
                 execution time and the computational complexity.
                 Further, the classifier is optimally trained by the
                 Hybrid MLFD algorithm which in turn reduces
                 optimization issues. The accuracy of the proposed
                 MLFD-based Deep-CNN using the SCID-2 dataset is
                 95.2683\% at 80\% of training and 97.6425\% for 10
                 K-fold. This manifests that the proposed MLFD-based
                 Deep-CNN outperforms all the conventional methods in
                 terms of accuracy, sensitivity, and specificity.",
  acknowledgement = ack-nhfb,
  articleno =    "2450024",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Xin:2024:SAC,
  author =       "Guangnan Xin and Min Zhu and Yuze Zhou and Guanyu
                 Jiang and Zeyu Cai and Aoyu Pang and And Qi Zhu",
  title =        "A Self-Attention {CycleGAN} for Cross-Domain
                 Semi-Supervised Contactless Palmprint Recognition",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500256",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500256",
  abstract =     "Nowadays, there is a growing concern about contactless
                 palmprint recognition because of its high-recognition
                 rate, efficiency, and convenience. With the development
                 of image acquisition equipment, it is an often case
                 that the palmprint images for identification and for
                 registration are captured by different devices. At the
                 same time, a large amount of well-labeled palmprint
                 images are difficult to collect. Therefore, the
                 performance of most existing contactless palmprint
                 recognition methods will be poor in real-life
                 applications. To address these issues, we proposed a
                 self-attention CycleGAN for cross-domain
                 semi-supervised palmprint recognition. Based on
                 CycleGAN, the styles of contactless palmprint images in
                 source domain and target domain can be swapped.
                 Specifically, the spatial features are captured through
                 self-attention modules by modeling long-range
                 dependencies. In addition, an extra source domain
                 classifier is trained with the labeled source domain
                 images to give the unlabeled images in target domain a
                 pseudo-label, by which images in target domain are
                 efficiently utilized. The experiment results showed
                 that our method achieved competitive performance.",
  acknowledgement = ack-nhfb,
  articleno =    "2450025",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2024:FSP,
  author =       "Yun Liu",
  title =        "Fault Signal Perception of Nanofiber Sensor for {$3$D}
                 Human Motion Detection Using Multi-Task Deep Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "02",
  pages =        "??--??",
  month =        mar,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467825500603",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:54 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467825500603",
  abstract =     "Once a fault occurs in the nanofiber sensor, the
                 scientific and reliable three-dimensional (3D) human
                 motion detection results will be compromised. It is
                 necessary to accurately and rapidly perceive the fault
                 signals of the nanofiber sensor and determine the type
                 of fault, to enable it to continue operating in a
                 sustained and stable manner. Therefore, we propose a
                 fault signal perception method for 3D human motion
                 detection nanofiber sensor based on multi-task deep
                 learning. First, through obtaining the fault
                 characteristic parameters of the nanofiber sensor, the
                 fault of the nanofiber sensor is reconstructed to
                 complete the fault location of the nanofiber sensor.
                 Second, the fault signal of the nanofiber sensor is
                 mapped by the penalty function, and the feature
                 extraction model of the fault signal of the nanofiber
                 sensor is constructed by combining the multi-task deep
                 learning. Finally, the multi-task deep learning
                 algorithm is used to calculate the sampling frequency
                 of the fault signal, and the key variable information
                 of the fault of the nanofiber sensor is extracted
                 according to the amplitude of the state change of the
                 nanofiber sensor, to realize the perception of the
                 fault signal of the nanofiber sensor. The results show
                 that the proposed method can accurately perceive the
                 fault signal of a nanofiber sensor in 3D human motion
                 detection, the maximum sensor fault location accuracy
                 is 97\%, and the maximum noise content of the fault
                 signal is only 5 dB, which shows that the method can be
                 widely used in fault signal perception.",
  acknowledgement = ack-nhfb,
  articleno =    "2550060",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Chukka:2024:BSM,
  author =       "Demudu Naidu Chukka and James Stephen Meka and S.
                 Pallam Setty and And Praveen Babu Choppala",
  title =        "{Bayesian} Selective Median Filtering for Reduction of
                 Impulse Noise in Digital Color Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467824500268",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500268",
  abstract =     "The focus of this paper is impulse noise reduction in
                 digital color images. The most popular noise reduction
                 schemes are the vector median filter and its many
                 variants that operate by minimizing the aggregate
                 distance from one pixel to every other pixel in a
                 chosen window. This minimizing operation determines the
                 most confirmative pixel based on its similarity to the
                 chosen window and replaces the central pixel of the
                 window with the determined one. The peer group filters,
                 unlike the vector median filters, determine a set of
                 pixels that are most confirmative to the window and
                 then perform filtering over the determined set. Using a
                 set of pixels in the filtering process rather than one
                 pixel is more helpful as it takes into account the full
                 information of all the pixels that seemingly contribute
                 to the signal. Hence, the peer group filters are found
                 to be more robust to noise. However, the peer group for
                 each pixel is computed deterministically using
                 thresholding schemes. A wrong choice of the threshold
                 will easily impair the filtering performance. In this
                 paper, we propose a peer group filtering approach using
                 principles of Bayesian probability theory and
                 clustering. Here, we present a method to compute the
                 probability that a pixel value is clean (not corrupted
                 by impulse noise) and then apply clustering on the
                 probability measure to determine the peer group. The
                 key benefit of this proposal is that the need for
                 thresholding in peer group filtering is completely
                 avoided. Simulation results show that the proposed
                 method performs better than the conventional vector
                 median and peer group filtering methods in terms of
                 noise reduction and structural similarity, thus
                 validating the proposed approach.",
  acknowledgement = ack-nhfb,
  articleno =    "2450026",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{He:2024:LBH,
  author =       "Shuhan He and Xueming Li and And Qiang Fu",
  title =        "{Laplace}-Based {$3$D} Human Mesh Sequence
                 Compression",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S021946782450027X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782450027X",
  abstract =     "Three-dimensional (3D) human mesh sequences obtained
                 by 3D scanning equipment are often used in film and
                 television, games, the internet, and other industries.
                 However, due to the dense point cloud data obtained by
                 3D scanning equipment, the data of a single frame of a
                 3D human model is always large. Considering the
                 different topologies of models between different
                 frames, and even the interaction between the human body
                 and other objects, the content of 3D models between
                 different frames is also complex. Therefore, the
                 traditional 3D model compression method always cannot
                 handle the compression of the 3D human mesh sequence.
                 To address this problem, we propose a sequence
                 compression method of 3D human mesh sequence based on
                 the Laplace operator, and test it on the complex
                 interactive behavior of a soccer player bouncing the
                 ball. This method first detects the mesh separation
                 degree of the interactive object and human body, and
                 then divides the sequence into a series of fragments
                 based on the consistency of separation degrees. In each
                 fragment, we employ a deformation algorithm to map
                 keyframe topology to other frames, to improve the
                 compression ratio of the sequence. Our work can be used
                 for the storage of mesh sequences and mobile
                 applications by providing an approach for data
                 compression.",
  acknowledgement = ack-nhfb,
  articleno =    "2450027",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Bai:2024:ODN,
  author =       "G. Mercy Bai and P. Venkadesh",
  title =        "Optimized Deep Neuro-Fuzzy Network with {MapReduce}
                 Architecture for Acute Lymphoblastic Leukemia
                 Classification and Severity Analysis",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500281",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500281",
  abstract =     "The most common life-threatening disease, acute
                 lymphoblastic leukemia (ALL), can be lethal within a
                 few weeks if untreated. The early detection and
                 analysis of leukemia is a key dilemma in the field of
                 disease diagnosis, and the methods available for the
                 classification process are time-consuming. To overcome
                 the issues, this paper develops a robust classification
                 technique named Horse Herd Whale Optimization-enabled
                 Deep Neuro-Fuzzy Network (HHWO-enabled DNFN method) for
                 ALL classification and severity analysis using the
                 MapReduce framework. The input image is first
                 preprocessed and segmented, and the useful features
                 necessary for improving the classification performance
                 are extracted during the mapper phase, known as HHWO,
                 which incorporates Horse Herd Optimization Algorithm
                 (HOA) and Whale Optimization Algorithm (WOA). Finally,
                 severity analysis of ALL is done to classify the levels
                 of leukemia to offer optimal treatment. As a result,
                 the developed method performed better than other
                 existing methods, achieving superior performance with a
                 greater testing accuracy of 0.959, sensitivity of
                 0.965, and specificity of 0.966, respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2450028",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Joseph:2024:MFT,
  author =       "Jovi Joseph and S. R. Sreela",
  title =        "{MODCN}: Fine-Tuned Deep Convolutional Neural Network
                 with {GAN} Deployed to Forecast Diabetic Eye Damage in
                 Fundus Retinal Images",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500293",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500293",
  abstract =     "Diabetic Retinopathy (DR) and Glaucoma are two of the
                 most common causes of vision loss world-wide. However,
                 it can be averted if therapy is begun early enough. In
                 biomedical applications, the use of digital image
                 processing has assisted in the automated identification
                 of some ailments at an earlier stage. To make this
                 prediction generally neural network classifier models
                 were previously used, but these models have the
                 drawback of being unable to detect multiple illnesses
                 that occur in the eye at the same time and require a
                 big database for successful classifier training. As a
                 result, a model is needed to reliably distinguish DR
                 and Glaucoma in diabetic individuals more accurately
                 and with minimum dataset images. In this view, this
                 study introduced Mayfly Optimized Deep Convolutional
                 Network (MODCN) model for automated disease detection
                 in the fundus retina images. In the MODCN model, the
                 images are initially preprocessed, segmented at
                 generator in the GAN model then a discriminator readily
                 gives synthesis of real images of the fundus retina,
                 thus a wide database has been created and considered as
                 training images for the MODCN classifier. MODCN
                 classifier has a modified high-density layer as a
                 transition layer to avoid overfitting and the errors
                 are minimized by tuning the hyperparameters using
                 Mayfly Optimization Algorithm. After feature mapping,
                 the classes normal, DR and Glaucoma are labeled and
                 stored. At the testing stage, images are preprocessed,
                 feature mapped and classified in the MODCN model. Thus,
                 the proposed MODCN model detects multiple illness such
                 as Diabetic Retinopathy and Glaucoma at the same time
                 even with a small amount of database that performs a
                 successful classifier training. This model is then
                 evaluated and gives an accuracy of 99\% that was higher
                 compared to previous models.",
  acknowledgement = ack-nhfb,
  articleno =    "2450029",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Shanmugasundaram:2024:DAI,
  author =       "Suresh Shanmugasundaram and Natarajan Palaniappan",
  title =        "Detection Accuracy Improvement on One-Stage Object
                 Detection Using {AP}-Loss-Based Ranking Module and
                 {ResNet-152} Backbone",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S021946782450030X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782450030X",
  abstract =     "Localization-loss and classification-loss are
                 optimized at the same time to train the one-stage
                 object detectors. Because of the large number of
                 anchors, the severe foreground--background class
                 disproportion causes significant classification-loss.
                 This paper discusses using a ranking module instead of
                 the classification module to mitigate this difficulty
                 and also Average-Precision loss (AP-loss) is utilized
                 on the ranking module. An optimization algorithm is
                 used to make the AP-loss as effective as possible.
                 Optimization algorithm blends the error-driven updating
                 method of perceptron learning and the deep network
                 backpropagation technique. This optimization algorithm
                 handles the foreground--background class disproportion
                 issues. One-stage detector with AP-loss and backbone
                 with ResNet-152 attains improvement in the detection
                 performance compared to the classification-losses-based
                 detectors.",
  acknowledgement = ack-nhfb,
  articleno =    "2450030",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Begum:2024:MDA,
  author =       "Afiya Parveen Begum and Prabha Selvaraj",
  title =        "Multiclass Diagnosis of {Alzheimer}'s Disease Analysis
                 Using Machine Learning and Deep Learning Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500311",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500311",
  abstract =     "Alzheimer's disease (AD) is a popular neurological
                 disorder affecting a critical part of the world's
                 population. Its early diagnosis is extremely imperative
                 for enhancing the quality of patients' lives. Recently,
                 improved technologies like image processing, artificial
                 intelligence involving machine learning, deep learning,
                 and transfer learning have been introduced for
                 detecting AD. This review describes the contribution of
                 image processing, feature extraction, optimization, and
                 classification approach in AD recognition. It deeply
                 investigates different methods adopted for multiclass
                 diagnosis of AD. The paper further presents a brief
                 comparison of existing AD studies in terms of
                 techniques adopted, performance measures,
                 classification accuracy, publication year, and
                 datasets. It then summarizes the important technical
                 barriers in reviewed works. This paper allows the
                 readers to gain profound knowledge regarding AD
                 diagnosis for promoting extensive research in this
                 field.",
  acknowledgement = ack-nhfb,
  articleno =    "2450031",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2024:CAP,
  author =       "S. Sathish Kumar and An. Sigappi and G. Arun Sampaul
                 Thomas and Y. Harold Robinson and And S. P. Raja",
  title =        "Classification and Analysis of Pistachio Species
                 Through Neural Embedding-Based Feature Extraction and
                 Small-Scale Machine Learning Techniques",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500323",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500323",
  abstract =     "Pistachios are a tremendous source of fiber, protein,
                 antioxidants, healthy fats, and other minerals like
                 thiamine and vitamin B6. They may help people lose
                 weight, lower cholesterol, and blood sugar levels, lead
                 to better gut, eye, and blood vessel health. The two
                 main varieties farmed and exported in Turkey are
                 kirmizi and siirt pistachios. Understanding how to
                 detect the type of pistachio is essential as it plays
                 an important role in trade. In this study, it is aimed
                 to classify these two types of pistachios and analyze
                 the performance of the various small-scale machine
                 learning algorithms. 2148 sample images for these two
                 kinds of pistachios were considered for this study
                 which includes 1232 of Kirmizi type and 916 of Siirt
                 type. In order to evaluate the model fairly, stratified
                 random sampling is applied on the dataset. For feature
                 extraction, we used deep neural network-based
                 embeddings to acquire the vector representation of
                 images. The classification of pistachio species is then
                 performed using a variety of small-scale machine
                 learning algorithms$^{29, 31}$ that have been trained
                 using these feature vectors. As a result of this study,
                 the success rate obtained from Logistic Regression
                 through features extracted from the penultimate layer
                 of Painters network is 97.20\%. The performance of the
                 models was evaluated through Class Accuracy, Precision,
                 Recall, F1 Score, and values of Area under the curve
                 (AUC). The outcomes show that the method suggested in
                 this study may quickly and precisely identify different
                 varieties of pistachios while also meeting agricultural
                 production needs.",
  acknowledgement = ack-nhfb,
  articleno =    "2450032",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Sau:2024:ORE,
  author =       "Paresh Chandra Sau and Manish Gupta and And Atul
                 Bansal",
  title =        "Optimized {ResUNet++}-Enabled Blood Vessel
                 Segmentation for Retinal Fundus Image Based on Hybrid
                 Meta-Heuristic Improvement",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500335",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500335",
  abstract =     "In recent years, several studies have undergone
                 automatic blood vessel segmentation based on
                 unsupervised and supervised algorithms to reduce user
                 interruption. Deep learning networks have been used to
                 get highly accurate segmentation results. However, the
                 incorrect segmentation of pathological information and
                 low micro-vascular segmentation is considered the
                 challenges present in the existing methods for
                 segmenting the retinal blood vessel. It also affects
                 different degrees of vessel thickness, contextual
                 feature fusion in technique, and perception of details.
                 A deep learning-aided method has been presented to
                 address these challenges in this paper. In the first
                 phase, the preprocessing is performed using the retinal
                 fundus images employed by the black ring removal, LAB
                 conversion, CLAHE-based contrast enhancement, and
                 grayscale image. Thus, the blood vessel segmentation is
                 performed by a new deep learning model termed optimized
                 ResUNet++. As an improvement to this deep learning
                 architecture, the activation function is optimized by
                 the J-AGSO algorithm. The objective function for the
                 optimized ResUNet++-based blood vessel segmentation is
                 to minimize the binary cross-entropy loss function.
                 Further, the post-processing of the images is carried
                 out by median filtering and binary thresholding. By
                 verifying the standard benchmark datasets, the proposed
                 model outperforms and attains enhanced performance.",
  acknowledgement = ack-nhfb,
  articleno =    "2450033",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Mulchandani:2024:EIE,
  author =       "Mona Mulchandani and Pramod S. Nair",
  title =        "{EBMICQL}: Improving Efficiency of Blockchain Miner
                 Pools via Incremental and Continuous {$Q$}-Learning
                 Framework",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500347",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
                 https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500347",
  abstract =     "Blockchain mining pools assist in reducing
                 computational load on individual miner nodes via
                 distributing mining tasks. This distribution must be
                 done in a non-redundant manner, so that each miner is
                 able to calculate block hashes with optimum efficiency.
                 To perform this task, a wide variety of mining
                 optimization methods are proposed by researchers, and
                 most of them distribute mining tasks via statistical
                 request processing models. These models segregate
                 mining requests into non-redundant sets, each of which
                 will be processed by individual miners. But this
                 division of requests follows a static procedure, and
                 does not consider miner specific parameters for set
                 creation, due to which overall efficiency of the
                 underlying model is limited, which reduces its mining
                 performance under real-time scenarios. To overcome this
                 issue, an Incremental & Continuous Q-Learning Framework
                 for generation of miner-specific task groups is
                 proposed in this text. The model initially uses a
                 Genetic Algorithm (GA) method to improve individual
                 miner performance, and then applies Q-Learning to
                 individual mining requests. The Reason for selecting GA
                 model is that it assists in maintaining better
                 speed-to-power (S2P) ratio by optimization of miner
                 resources that are utilized during computations. While,
                 the reason for selecting Q-Learning Model is that it is
                 able to continuously identify miners performance, and
                 create performance-based mining pools at a per-miner
                 level. Due to application of Q-Learning, the model is
                 able to assign capability specific mining tasks to
                 individual miner nodes. Because of this
                 capability-driven approach, the model is able to
                 maximize efficiency of mining, while maintaining its
                 QoS performance. The model was tested on different
                 consensus methods including Practical Byzantine Fault
                 Tolerance Algorithm (PBFT), Proof-of-Work (PoW),
                 Proof-of-Stake (PoS), and Delegated PoS (DPoS), and its
                 performance was evaluated in terms of mining delay,
                 miner efficiency, number of redundant calculations per
                 miner, and energy efficiency for mining nodes. It was
                 observed that the proposed GA based Q-Learning Model
                 was able to reduce mining delay by 4.9\%, improve
                 miners efficiency by 7.4\%, reduce number of redundant
                 computations by 3.5\%, and reduce energy required for
                 mining by 7.1\% when compared with various
                 state-of-the-art mining optimization techniques.
                 Similar performance improvement was observed when the
                 model was applied on different blockchain deployments,
                 thus indicating better scalability and deployment
                 capability for multiple application scenarios.",
  acknowledgement = ack-nhfb,
  articleno =    "2450034",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Muraleedharan:2024:CUN,
  author =       "K. M. Muraleedharan and K. T. Bibish Kumar and Sunil
                 John and And R. K. Sunil Kumar",
  title =        "Combined Use of Nonlinear Measures for Analyzing
                 Pathological Voices",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500359",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500359",
  abstract =     "Automatic voice pathology detection enables an
                 objective assessment of pathologies that influence the
                 voice production strategy. By utilizing the
                 conventional pipeline model as well as the modern deep
                 learning-centric end-to-end methodology, numerous
                 pathological voice analyzing techniques have been
                 developed. The conventional methodology is still a
                 valid choice owing to the lack of enormous amounts of
                 training data in the study region of pathological
                 voice. In the meantime, obtaining higher precision,
                 higher accuracy, and stability is still a complicated
                 task. Therefore, by amalgamating the nonlinear measure,
                 the pathological voices are analyzed to abate such
                 risks. The viability of six nonlinear discriminating
                 measures derived from the phase space realm, involving
                 healthy and pathological voice signals, is studied in
                 this work. The analyzed parameters are Singularity
                 spectrum coefficients ($ \alpha_{\rm min}, \alpha_{\rm
                 max}, \gamma_1 $ and $ \gamma_2$). Correlation entropy
                 at optimum embedding dimension ($ K_{2m}$) and
                 correlation dimension at optimum embedding dimension ($
                 D_{2m}$). Analyzing the pathological voices with better
                 accuracy rates is the major objective of the proposed
                 methodology. Here, the Support Vector Machine (SVM) was
                 utilized as the classifier. Experimentations were
                 performed on VOiceICarfEDerico (VOICED) databases
                 subsuming 208 healthy, as well as pathological voices,
                 amongst these 50 samples, were utilized. Here, the
                 model obtained 97\% of accuracy with 99\% as of the
                 classifier with Gaussian kernel function. Therefore, to
                 differentiate normal as well as pathological subjects,
                 the six proposed characteristics are highly beneficial;
                 in addition, they will be supportive in pathology
                 diagnosis.",
  acknowledgement = ack-nhfb,
  articleno =    "2450035",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Liu:2024:ICL,
  author =       "Fuxiang Liu and Chen Zang and Junqi Shi and Weiyu He
                 and Yupeng Liang and And Lei Li",
  title =        "An Improved {COVID-19} Lung {X}-Ray Image
                 Classification Algorithm Based on {ConvNeXt} Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500360",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500360",
  abstract =     "Aiming at the new coronavirus that appeared in 2019,
                 which has caused a large number of infected patients
                 worldwide due to its high contagiousness, in order to
                 detect the source of infection in time and cut off the
                 chain of transmission, we developed a new Chest X-ray
                 (CXR) image classification algorithm with high
                 accuracy, simple operation and fast processing for
                 COVID-19. The algorithm is based on ConvNeXt pure
                 convolutional neural network, we adjusted the network
                 structure and loss function, added some new Data
                 Augmentation methods and introduced attention
                 mechanism. Compared with other classical convolutional
                 neural network classification algorithms such as
                 AlexNet, ResNet-34, ResNet-50, ResNet-101,
                 ConvNeXt-tiny, ConvNeXt-small and ConvNeXt-base, the
                 improved algorithm has better performance on COVID
                 dataset.",
  acknowledgement = ack-nhfb,
  articleno =    "2450036",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Fauzi:2024:FBO,
  author =       "Nurul Izzatie Husna Fauzi and Zalili Musa and And
                 Fadhl Hujainah",
  title =        "Feature-Based Object Detection and Tracking: a
                 Systematic Literature Review",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500372",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500372",
  abstract =     "Correct object detection plays a key role in
                 generating an accurate object tracking result.
                 Feature-based methods have the capability of handling
                 the critical process of extracting features of an
                 object. This paper aims to investigate object tracking
                 using feature-based methods in terms of (1) identifying
                 and analyzing the existing methods; (2) reporting and
                 scrutinizing the evaluation performance matrices and
                 their implementation usage in measuring the
                 effectiveness of object tracking and detection; (3)
                 revealing and investigating the challenges that affect
                 the accuracy performance of identified tracking
                 methods; (4) measuring the effectiveness of identified
                 methods in terms of revealing to what extent the
                 challenges can impact the accuracy and precision
                 performance based on the evaluation performance
                 matrices reported; and (5) presenting the potential
                 future directions for improvement. The review process
                 of this research was conducted based on standard
                 systematic literature review (SLR) guidelines by
                 Kitchenam's and Charters'. Initially, 157 prospective
                 studies were identified. Through a rigorous study
                 selection strategy, 32 relevant studies were selected
                 to address the listed research questions. Thirty-two
                 methods were identified and analyzed in terms of their
                 aims, introduced improvements, and results achieved,
                 along with presenting a new outlook on the
                 classification of identified methods based on the
                 feature-based method used in detection and tracking
                 process.",
  acknowledgement = ack-nhfb,
  articleno =    "2450037",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Li:2024:ATM,
  author =       "Zhipeng Li and Jun Wang and Lijun Hua and Honghui Liu
                 and And Wenli Song",
  title =        "Automatic Tracking Method for {$3$D} Human Motion Pose
                 Using Contrastive Learning",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "03",
  pages =        "??--??",
  month =        may,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467825500378",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Wed Jun 5 09:06:55 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467825500378",
  abstract =     "Automatic tracking of three-dimensional (3D) human
                 motion pose has the potential to provide corresponding
                 technical support in various fields. However, existing
                 methods for tracking human motion pose suffer from
                 significant errors, long tracking times and suboptimal
                 tracking results. To address these issues, an automatic
                 tracking method for 3D human motion pose using
                 contrastive learning is proposed. By using the feature
                 parameters of 3D human motion poses, threshold
                 variation parameters of 3D human motion poses are
                 computed. The golden section is introduced to transform
                 the threshold variation parameters and extract the
                 features of 3D human motion poses by comparing the
                 feature parameters with the threshold of parameter
                 variation. Under the supervision of contrastive
                 learning, a constraint loss is added to the
                 local--global deep supervision module of contrastive
                 learning to extract local parameters of 3D human motion
                 poses, combined with their local features. After
                 normalizing the 3D human motion pose images, frame
                 differences of the background image are calculated. By
                 constructing an automatic tracking model for 3D human
                 motion poses, automatic tracking of 3D human motion
                 poses is achieved. Experimental results demonstrate
                 that the highest tracking lag is 9\%, there is no
                 deviation in node tracking, the pixel contrast is
                 maintained above 90\% and only 6 sub-blocks have detail
                 loss. This indicates that the proposed method
                 effectively tracks 3D human motion poses, tracks all
                 the nodes, achieves high accuracy in automatic tracking
                 and produces good tracking results.",
  acknowledgement = ack-nhfb,
  articleno =    "2550037",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kumar:2024:NIR,
  author =       "Kattela Pavan Kumar and Matcha Venu Gopala Rao and And
                 Moram Venkatanarayana",
  title =        "A Novel Image Recovery from Moving Water Surface Using
                 Multi-Objective Bispectrum Method",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1142/S0219467824500384",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500384",
  abstract =     "Nowadays, the image degradation field suffers from
                 several challenges while processing underwater color
                 images including color distortion and image blurring
                 due to the scattering media. Moreover, to get
                 appropriate multi-frame super-resolution images, there
                 is essential for recovering a better quantity of
                 images. Traditionally, the shift among images is
                 directly evaluated when considering the under-sampled
                 Low-Resolution (LR) images. On the other hand, the
                 high-frequency LR image faces unreliability owing to
                 the aliasing consequences of sub-sampling, but it will
                 also degrade the recovery accuracy. This task design
                 implements a novel image recovery model from the moving
                 water surface by adopting the multi-objective adaptive
                 higher-order spectral analysis. Image pre-processing,
                 lucky region selection, and image recovery are the
                 three main phases of this model. The bicoherence method
                 and dice coefficient method are adopted for performing
                 the lucky region selection. Finally, the adoption of
                 the multi-objective adaptive bispectra method is used
                 for performing the image recovery from the moving water
                 surface. The improved Adaptive Fitness-oriented Random
                 number-based Galactic Swarm Optimization (AFR-GSO)
                 algorithm is used for optimizing the constraints of the
                 bispectrum method. The experimental results verify the
                 enrichment of image quality by the proposed model over
                 the existing techniques.",
  acknowledgement = ack-nhfb,
  articleno =    "2450038",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Veling:2024:MDC,
  author =       "Shripad S. Veling and T. B. Mohite-patil",
  title =        "Multi-Disease Classification of Mango Tree Using
                 Meta-Heuristic-Based Weighted Feature Selection and
                 {LSTM} Model",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500396",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500396",
  abstract =     "Global food security can be influenced by the diseases
                 in crop plants as several diseases straightforwardly
                 influence the quality of the grains, vegetables,
                 fruits, etc., which also results in affecting of
                 agricultural productivity. Like other plants, the mango
                 tree is also affected by several diseases, and also the
                 identification of multi-disease classification with a
                 single leaf is more complex, and also it is impossible
                 to detect diseases with bare eyes. Based on the other
                 plants, the mango tree is also affected by various
                 diseases, which is more difficult to detect the
                 disorders with bare eyes. It is error-prone,
                 inconsistent, and unreliable. Here, the mango trees are
                 affected during the production, and also affect the
                 plant health regarding multi-diseases. When the plants
                 are affected by the diseases, it may cause fewer
                 amounts of productivity, as a result, impacting the
                 economy. However, it is more critical to detect plant
                 diseases with the large varieties of trees and plants.
                 Various research tasks on deep learning approaches
                 focus on identifying the diseases in plants including
                 leaves and fruits. Thus, the main objective of this
                 paper is to implement an effective and appropriate
                 technique for diagnosing mango tree diseases and their
                 symptoms through fruit and leaf images, and thus, there
                 is a need for an appropriate system for cost-effective
                 and early solutions to this problem. Hence, the main
                 intention of this work is to implement an efficient and
                 suitable technique for diagnosing mango tree diseases
                 and also identify the symptoms through fruit and leaf
                 images. Intending to overcome the existing challenges,
                 there is a need for an appropriate system for achieving
                 cost-effectiveness and also creating an early solution
                 to resolve this problem. This paper intends to present
                 novel deep learning models for mango tree multi-disease
                 classification. Initially, the data collection is done
                 for gathering the diseased parts of the mango tree in
                 terms of leaf and fruit images. Then, the contrast
                 enhancement of the images is performed by the
                 ``Contrast-Limited Adaptive Histogram Equalization
                 (CLAHE)''. For the deep feature extraction of leaf
                 images, and fruit images, Convolutional Neural Network
                 (CNN) is employed, and the features from both inputs
                 are concatenated for further processing. Further, the
                 weighted feature selection is adopted for selecting the
                 most significant features by the Adaptive Squirrel-Grey
                 Wolf Search Optimization (AS-GWSO). Enhanced ``Long
                 Short Term Memory (LSTM)'' is applied in the
                 classification part with parameter optimization using
                 the same AS-GWSO for enhancing classification accuracy.
                 At last, the results of the designed system on various
                 mango tree diseases verify that the designed approach
                 has yielded the highest accuracy by evaluating
                 conventional approaches. Therefore, it would also
                 alleviate and treat the affected mango leaf diseases
                 accurately.",
  acknowledgement = ack-nhfb,
  articleno =    "2450039",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Hamdi:2024:CAC,
  author =       "Dhekra {El Hamdi} and Ines Elouedi and And Ihsen
                 Slim",
  title =        "Computer-Aided Classification of Cell Lung Cancer Via
                 {PET\slash CT} Images Using Convolutional Neural
                 Network",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500402",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500402",
  abstract =     "Lung cancer is the leading cause of cancer-related
                 death worldwide. Therefore, early diagnosis remains
                 essential to allow access to appropriate curative
                 treatment strategies. This paper presents a novel
                 approach to assess the ability of Positron Emission
                 Tomography/Computed Tomography (PET/CT) images for the
                 classification of lung cancer in association with
                 artificial intelligence techniques. We have built, in
                 this work, a multi output Convolutional Neural Network
                 (CNN) as a tool to assist the staging of patients with
                 lung cancer. The TNM staging system as well as
                 histologic subtypes classification were adopted as a
                 reference. The VGG 16 network is applied to the PET/CT
                 images to extract the most relevant features from
                 images. The obtained features are then transmitted to a
                 three-branch classifier to specify Nodal (N), Tumor (T)
                 and histologic subtypes classification. Experimental
                 results demonstrated that our CNN model achieves good
                 results in TN staging and histology classification. The
                 proposed architecture classified the tumor size with a
                 high accuracy of 0.94 and the area under the curve
                 (AUC) of 0.97 when tested on the Lung-PET-CT-Dx
                 dataset. It also has yielded high performance for N
                 staging with an accuracy of 0.98. Besides, our approach
                 has achieved better accuracy than state-of-the-art
                 methods in histologic classification.",
  acknowledgement = ack-nhfb,
  articleno =    "2450040",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prasad:2024:FSC,
  author =       "Rajesh S. Prasad and Jayashree Rajesh Prasad and
                 Bhushan S. Chaudhari and Nihar M. Ranjan and And Rajat
                 Srivastava",
  title =        "{FCM} with Spatial Constraint Multi-Kernel
                 Distance-Based Segmentation and Optimized Deep Learning
                 for Flood Detection",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500414",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500414",
  abstract =     "Floods are the deadly and catastrophic disasters,
                 causing loss of life and harm to assets, farmland, and
                 infrastructure. To address this, it is necessary to
                 devise and employ an effective flood management system
                 that can immediately identify flood areas to initiate
                 relief measures as soon as possible. Therefore, this
                 research work develops an effective flood detection
                 method, named Anti- Corona-Shuffled Shepherd
                 Optimization Algorithm-based Deep Quantum Neural
                 Network (ACSSOA-based Deep QNN) for identifying the
                 flooded areas. Here, the segmentation process is
                 performed using Fuzzy C-Means with Spatial Constraint
                 Multi-Kernel Distance (MKFCM\_S) wherein the Fuzzy
                 C-Means (FCM) is modified with Spatial Constraints
                 Based on Kernel-Induced Distance (KFCM\_S). For flood
                 detection, Deep QNN has been used wherein the training
                 progression of Deep QNN is done using designed
                 optimization algorithm, called ACSSOA. Besides, the
                 designed ACSSOA is newly formed by the hybridization of
                 Anti Corona Virus Optimization (ACVO) and Shuffled
                 Shepherd Optimization Algorithm (SSOA). The devised
                 method was evaluated using the Kerala Floods database,
                 and it acquires the segmentation accuracy, testing
                 accuracy, sensitivity, and specificity with highest
                 values of 0.904, 0.914, 0.927, and 0.920,
                 respectively.",
  acknowledgement = ack-nhfb,
  articleno =    "2450041",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Khaki:2024:RCN,
  author =       "Ali Khaki",
  title =        "Robust Convolutional Neural Network Based on {UNet}
                 for Iris Segmentation",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500426",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500426",
  abstract =     "Nowadays, the iris recognition system is one of the
                 most widely used and most accurate biometric systems.
                 The iris segmentation is the most crucial stage of iris
                 recognition system. The accurate iris segmentation can
                 improve the efficiency of iris recognition. The main
                 objective of iris segmentation is to obtain the iris
                 area. Recently, the iris segmentation methods based on
                 convolutional neural networks (CNNs) have been grown,
                 and they have improved the accuracy greatly.
                 Nevertheless, their accuracy is decreased by
                 low-quality images captured in uncontrolled conditions.
                 Therefore, the existing methods cannot segment
                 low-quality images precisely. To overcome the
                 challenge, this paper proposes a robust convolutional
                 neural network (R-Net) inspired by UNet for iris
                 segmentation. R-Net is divided into two parts: encoder
                 and decoder. In this network, several layers are added
                 to ResNet-34, and used in the encoder path. In the
                 decoder path, four convolutions are applied at each
                 level. Both help to obtain suitable feature maps and
                 increase the network accuracy. The proposed network has
                 been tested on four datasets: UBIRIS v2 (UBIRIS), CASIA
                 iris v4.0 (CASIA) distance, CASIA interval, and IIT
                 Delhi v1.0 (IITD). UBIRIS is a dataset that is used for
                 low-quality images. The error rate (NICE1) of proposed
                 network is 0.0055 on UBIRIS, 0.0105 on CASIA interval,
                 0.0043 on CASIA distance, and 0.0154 on IITD. Results
                 show better performance of the proposed network
                 compared to other methods.",
  acknowledgement = ack-nhfb,
  articleno =    "2450042",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Jannu:2024:SAN,
  author =       "Chaitanya Jannu and Sunny Dayal Vanambathina",
  title =        "Shuffle Attention {$U$}-Net for Speech Enhancement in
                 Time Domain",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500438",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500438",
  abstract =     "Over the past 10 years, deep learning has enabled
                 significant advancements in the improvement of noisy
                 speech. In an end-to-end speech enhancement, the deep
                 neural networks transform a noisy speech signal to a
                 clean speech signal in the time domain directly without
                 any conversion or estimation of mask. Recently, the
                 U-Net-based models achieved good enhancement
                 performance. Despite this, some of them may neglect
                 context-related information and detailed features of
                 input speech in case of ordinary convolution. To
                 address the above issues, recent studies have upgraded
                 the performance of the model by adding various network
                 modules such as attention mechanisms, long and
                 short-term memory (LSTM). In this work, we propose a
                 new U-Net-based speech enhancement model using a novel
                 lightweight and efficient Shuffle Attention (SA), Gated
                 Recurrent Unit (GRU), residual blocks with dilated
                 convolutions. Residual block will be followed by a
                 multi-scale convolution block (MSCB). The proposed
                 hybrid structure enables the temporal context
                 aggregation in time domain. The advantage of shuffle
                 attention mechanism is that the channel and spatial
                 attention are carried out simultaneously for each
                 sub-feature in order to prevent potential noises while
                 also highlighting the proper semantic feature areas by
                 combining the same features from all locations. MSCB is
                 employed for extracting rich temporal features. To
                 represent the correlation between neighboring noisy
                 speech frames, a two Layer GRU is added in the
                 bottleneck of U-Net. The experimental findings
                 demonstrate that the proposed model outperformed the
                 other existing models in terms of short-time objective
                 intelligibility (STOI), and perceptual evaluation of
                 the speech quality (PESQ).",
  acknowledgement = ack-nhfb,
  articleno =    "2450043",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Visalini:2024:DES,
  author =       "K. Visalini and Saravanan Alagarsamy and And S. P.
                 Raja",
  title =        "Detecting Epileptic Seizures Using Symplectic Geometry
                 Decomposition-Based Features and {Gaussian} Deep
                 {Boltzmann} Machines",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S021946782450044X",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S021946782450044X",
  abstract =     "Studies deem that about 1 percent of the human
                 population is affected by epileptic seizures on a
                 global scale. It is characterized as an undue neuronal
                 discharge in the brain and degrades the quality of life
                 of the patients to a large extent. Children being
                 unaware of a sudden onset of seizures could be affected
                 by severe injury or even mortality.
                 Machine-learning-based epileptic seizure detection from
                 EEG (Electro-Encephalogram) signals have always been a
                 hot area of research. However, the majority of the
                 research works rely on correlated non-linear features
                 extracted from the EEG signals, causing a
                 high-computational overhead, and challenging their
                 application in real-time clinical diagnosis. This study
                 proposes a robust seizure detection framework using
                 Gaussian Deep Boltzmann Machine-based classifier and
                 Symplectic Geometric Decomposition (SGD)-based
                 features. The simplified eigenvalues derived through
                 Symplectic Similarity Transform (SST) are employed as
                 feature vectors for the classifier, eliminating the
                 need for a deliberate feature extraction procedure. The
                 study examines the transferability capability of the
                 suggested framework in discriminating seizures in both
                 neonates and pediatric subjects in unison,
                 experimenting with classical annotated datasets. The
                 model yielded a mean accuracy of about 97.91\% and an
                 F1 Score of 0.935 in pediatric seizure detection, and
                 mean sensitivity and specificity of 99.05\% and
                 98.28\%, in neonatal seizure detection tasks,
                 respectively. Thus, the model can be deemed comparable
                 to the available state-of-the-art seizure detection
                 frameworks.",
  acknowledgement = ack-nhfb,
  articleno =    "2450044",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Prashanthi:2024:HOB,
  author =       "M. Prashanthi and M. Chandra Mohan",
  title =        "Hybrid Optimization-Based Neural Network Classifier
                 for Software Defect Prediction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500451",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500451",
  abstract =     "The software is applied in various areas so the
                 quality of the software is very important. The software
                 defect prediction (SDP) is used to solve the software
                 issues and enhance the quality. The robustness and
                 reliability are the major concerns in the existing SDP
                 approaches. Hence, in this paper, the hybrid
                 optimization-based neural network (Optimized NN) is
                 developed for the effective detection of the defects in
                 the software. The two main steps involved in the
                 Optimized NN-based SDP are feature selection and SDP
                 utilizing Optimized NN. The data is fed forwarded to
                 the feature selection module, where relief algorithm
                 selects the significant features relating to the defect
                 and no-defects. The features are fed to the SDP module,
                 and the optimal tuning of NN classifier is obtained by
                 the hybrid optimization developed by the integration of
                 the social spider algorithm (SSA) and gray wolf
                 optimizer (GWO). The comparative analysis of the
                 developed prediction model reveals the effectiveness of
                 the proposed method that attained the maximum accuracy
                 of 93.64\%, maximum sensitivity of 95.14\%, maximum
                 specificity of 99\%, maximum $ F_1$-score of 93.53\%,
                 and maximum precision of 99\% by considering the
                 $K$-fold.",
  acknowledgement = ack-nhfb,
  articleno =    "2450045",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Kulkarni:2024:HBE,
  author =       "Girish Kulkarni and Chiranjeevi Manike",
  title =        "Heuristic-Based Ensemble Model Selection Strategy with
                 Parameter Tuning for Optimal {{\em Diabetes
                 mellitus\/}} Prediction",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500463",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500463",
  abstract =     "Diabetes is a terrible health situation characterized
                 by high-rise blood glucose levels. If it is not
                 predicted at an early stage, then it generates the
                 problems in the human body like kidney failure or
                 premature death, and stroke. Controlling blood glucose
                 levels provides patients with helpful dietary
                 recommendations, which are critical components of
                 diabetes management. In the past decades, diverse
                 conventional approaches have been executed to predict
                 the beginning stages of diabetes mellitus depending on
                 physical and substance tests. Still, developing a new
                 framework that can effectively diagnose diabetes
                 mellitus-affected patients is required. To this end,
                 the major target of this task is to predict diabetes
                 mellitus with an advanced accuracy rate with the help
                 of the Heuristic-based Ensemble Model Selection
                 Strategy (H-EMSS). In the data collection phase, the
                 Pima Indian Diabetes dataset (PID) is taken from the
                 storage area of UCI. The data cleaning is performed in
                 the pre-processing stage, which is the technique of
                 removing or fixing, corrupted, incorrect, duplicate,
                 incomplete data, or incorrectly formatted, inside a
                 dataset. Then, the diabetes prediction is accomplished
                 by the H-EMSS. Here, 10 base learners like Naive Bayes
                 (NB), Convolutional Neural Network (CNN), Linear
                 Regression (LR), Deep Neural Network (DNN), Support
                 Vector Machine (SVM), Artificial Neural Network (ANN),
                 Decision Tree (DT), Random Forest (RF), Auto Encoder
                 (AE) and Recurrent Neural Network (RNN) are considered.
                 From these, three classifiers are optimally selected by
                 the Modified Scalar Factor-based Elephant Herding
                 Optimization (MSF-EHO), so that the prediction rate
                 will be high. The suggested methodology's efficacy is
                 also compared and analyzed, with the findings
                 demonstrating the recommended model's superiority. The
                 overall evaluation is that the Root Mean Square Error
                 (RMSE) of the designed Modified Scalar Factor-based
                 Elephant Herding Optimization-Heuristic-based Ensemble
                 Model Selection Strategy (MSF-EHO-H-EMSS) attains
                 4.601\% and also the Mean Absolute Error (MAE) on the
                 designed method achieves 0.99\%. Thus, the given
                 outcomes of the designed method revealed that it
                 achieves elevated performance than the other existing
                 techniques regarding diverse error metrics.",
  acknowledgement = ack-nhfb,
  articleno =    "2450046",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Dharwadkar:2024:HEC,
  author =       "Nagaraj V. Dharwadkar and Ashutosh A. Lonikar and And
                 Mufti Mahmud",
  title =        "High Embedding Capacity Color Image Steganography
                 Scheme Using Pixel Value Differencing and Addressing
                 the Falling-Off Boundary Problem",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500475",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500475",
  abstract =     "In this paper, we changed the methodology for pixel
                 value differencing. The proposed method work on RGB
                 color images improves the existing PVD technique in
                 terms of embedding capacity and overcomes the issue of
                 falling off boundaries in the traditional PVD
                 technique, and provides security to the secret message
                 from histogram quantization attack. Color images are
                 composed of three different color channels (red, green,
                 and blue), so we cannot apply the traditional pixel
                 value differencing algorithm to them. Due to that, the
                 proposed technique divides the RGB photograph in red,
                 blue, and green channels. Following that the modified
                 pixel value differencing algorithm is employed to all
                 successive pixels of color channels. We get the total
                 embedding capacity by adding the embedding capacities
                 of each color component. After embedding the data, we
                 concatenate the color channels to get the stegoimage.
                 On a series of color images, we tested our pixel value
                 differencing approach and found that the
                 stego-picture's visual excellence and payload capacity
                 were reasonable. The variation in histogram between the
                 stego and cover photographs was minor, making it
                 resistant to histogram quantization attacks, and the
                 suggested approach also solves the issue of falling off
                 the boundary.",
  acknowledgement = ack-nhfb,
  articleno =    "2450047",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Lavanya:2024:ECM,
  author =       "V. Lavanya and P. Chandra Sekhar",
  title =        "Efficient Cybersecurity Model Using Wavelet Deep {CNN}
                 and Enhanced Rain Optimization Algorithm",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500487",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500487",
  abstract =     "Cybersecurity has received greater attention in modern
                 times due to the emergence of IoT (Internet-of-Things)
                 and CNs (Computer Networks). Because of the massive
                 increase in Internet access, various malicious malware
                 have emerged and pose significant computer security
                 threats. The numerous computing processes across the
                 network have a high risk of being tampered with or
                 exploited, which necessitates developing effective
                 intrusion detection systems. Therefore, it is essential
                 to build an effective cybersecurity model to detect the
                 different anomalies or cyber-attacks in the network.
                 This work introduces a new method known as {\em Wavelet
                 Deep Convolutional Neural Network (WDCNN)\/} to
                 classify cyber-attacks. The presented network combines
                 WDCNN with Enhanced Rain Optimization Algorithm (EROA)
                 to minimize the loss in the network. This proposed
                 algorithm is designed to detect attacks in large-scale
                 data and reduces the complexities of detection with
                 maximum detection accuracy. The proposed method is
                 implemented in PYTHON. The classification process is
                 completed with the help of the two most famous
                 datasets, KDD cup 1999 and CICMalDroid 2020. The
                 performance of WDCNN\_EROA can be assessed using
                 parameters like specificity, accuracy, precision
                 F-measure and recall. The results showed that the
                 proposed method is about 98.72\% accurate for the first
                 dataset and 98.64\% for the second dataset.",
  acknowledgement = ack-nhfb,
  articleno =    "2450048",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}

@Article{Wei:2024:AIS,
  author =       "Yanxi Wei",
  title =        "Artistic Image Style Transfer Based on {CycleGAN}
                 Network Model",
  journal =      j-INT-J-IMAGE-GRAPHICS,
  volume =       "24",
  number =       "04",
  pages =        "??--??",
  month =        jul,
  year =         "2024",
  DOI =          "https://doi.org/10.1142/S0219467824500499",
  ISSN =         "0219-4678",
  ISSN-L =       "0219-4678",
  bibdate =      "Sat Oct 19 15:24:03 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/ijig.bib",
  URL =          "https://www.worldscientific.com/doi/10.1142/S0219467824500499",
  abstract =     "With the development of computer technology, image
                 stylization has become one of the hottest technologies
                 in image processing. To optimize the effect of artistic
                 image style conversion, a method of artistic image
                 style conversion optimized by attention mechanism is
                 proposed. The CycleGAN network model is introduced, and
                 then the generator is optimized by the attention
                 mechanism. Finally, the application effect of the
                 improved model is tested and analyzed. The results show
                 that the improved model tends to be stable after 40
                 iterations, the loss value remains at 0.3, and the PSNR
                 value can reach up to 15. From the perspective of the
                 generated image effect, the model has a better visual
                 effect than the CycleGAN model. In the subjective
                 evaluation, 63 people expressed satisfaction with the
                 converted artistic image. As a result, the cyclic
                 generative adversarial network model optimized by the
                 attention mechanism improves the clarity of the
                 generated image, enhances the effect of blurring the
                 target boundary contour, retains the detailed
                 information of the image, optimizes the image
                 stylization effect, and improves the image quality of
                 the method and application value of the processing
                 field.",
  acknowledgement = ack-nhfb,
  articleno =    "2450049",
  fjournal =     "International Journal of Image and Graphics (IJIG)",
  journal-URL =  "http://www.worldscientific.com/worldscinet/ijig",
}