Genetic programming for edge detection based on figure of merit

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Fu:2012:GECCOcomp,
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Genetic programming for edge detection based on figure
                 of merit",
  booktitle =    "GECCO Companion '12: Proceedings of the fourteenth
                 international conference on Genetic and evolutionary
                 computation conference companion",
  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 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, Genetic programming: Poster",
  pages =        "1483--1484",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2331003",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The figure of merit (FOM) is popular for testing an
                 edge detector's performance, but there are very few
                 reports using FOM as an evaluation method in the
                 learning stage of supervised learning methods. In this
                 study, FOM is investigated as a fitness function in
                 Genetic Programming (GP) for edge detection. Since FOM
                 has some drawbacks from type II errors, new fitness
                 functions are developed based on FOM in order to
                 address these weaknesses. Experimental results show
                 that FOM can be used to evolve GP edge detectors that
                 perform better than the Sobel detector, and the new
                 fitness functions clearly improve the ability of GP
                 edge detectors to find edge points and give a single
                 response on edges, compared with the fitness function
                 using FOM.",
  notes =        "Also known as \cite{2331003} Distributed at
                 GECCO-2012.

                 ACM Order Number 910122.",
}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations