Genetic programming for edge detection using multivariate density

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

@InProceedings{Fu:2013:GECCO,
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Genetic programming for edge detection using
                 multivariate density",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "917--924",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463485",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The combination of local features in edge detection
                 can generally improve detection performance. However,
                 how to effectively combine different basic features
                 remains an open issue and needs to be investigated.
                 Multivariate density is a generalisation of the
                 one-dimensional (univariate) distribution to higher
                 dimensions. In order to effectively construct composite
                 features with multivariate density, a Genetic
                 Programming (GP) system is proposed to evolve
                 Bayesian-based programs. An evolved Bayesian-based
                 program estimates the relevant multivariate density to
                 construct a composite feature. The results of the
                 experiments show that the GP system constructs
                 high-level combined features which substantially
                 improve the detection performance.",
  notes =        "Also known as \cite{2463485} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",
}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations