Genetic programming for edge detection using blocks to extract features

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Genetic programming for edge detection using blocks to
                 extract features",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "855--862",
  keywords =     "genetic algorithms, genetic programming, genetics
                 based machine learning",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330282",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Single pixels can be directly used to construct
                 low-level edge detectors but these detectors are not
                 good for suppressing noise and some texture. In
                 general, features based on a small area are used to
                 suppress noise and texture. However, there is very
                 little guidance in the literature on how to select the
                 area size. In this paper, we employ Genetic Programming
                 (GP) to evolve edge detectors via automatically
                 searching for features based on flexible blocks rather
                 than dividing a fixed window into small areas based on
                 different directions. Experimental results for natural
                 images show that using blocks to extract features
                 obtains better performance than using single pixels
                 only to construct detectors, and that GP can
                 successfully choose the block size for extracting
  notes =        "Also known as \cite{2330282} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang