Genetic Programming for Automatic Construction of Variant Features in Edge Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Genetic Programming for Automatic Construction of
                 Variant Features in Edge Detection",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY,
                 EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR,
                 EvoRISK, EvoROBOT, EvoSTOC",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Anna I. Esparcia-Alcazar and Antonio Della Cioppa and 
                 Ivanoe {De Falco} and Ernesto Tarantino and 
                 Carlos Cotta and Robert Schaefer and Konrad Diwold and 
                 Kyrre Glette and Andrea Tettamanzi and 
                 Alexandros Agapitos and Paolo Burrelli and J. J. Merelo and 
                 Stefano Cagnoni and Mengjie Zhang and Neil Urquhart and Kevin Sim and 
                 Aniko Ekart and Francisco {Fernandez de Vega} and 
                 Sara Silva and Evert Haasdijk and Gusz Eiben and 
                 Anabela Simoes and Philipp Rohlfshagen",
  series =       "LNCS",
  volume =       "7835",
  publisher =    "Springer Verlag",
  address =      "Vienna",
  publisher_address = "Berlin",
  pages =        "354--364",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Edge
                 Detection, Feature Construction",
  isbn13 =       "978-3-642-37191-2",
  DOI =          "doi:10.1007/978-3-642-37192-9_36",
  size =         "11 pages",
  abstract =     "Basic features for edge detection, such as
                 derivatives, can be further manipulated to improve
                 detection performance. However, how to effectively
                 combine different basic features remains an open issue
                 and needs to be investigated. In this study, Genetic
                 Programming (GP) is used to automatically and
                 effectively construct rotation variant features based
                 on basic features from derivatives, F-test, and
                 histograms of images. To reduce computational cost in
                 the training stage, the basic features only use the
                 horizontal responses to construct new horizontal
                 features. These new features are then combined with
                 their own rotated versions in the vertical direction in
                 the testing stage. The experimental results show that
                 the rotation variant features constructed by GP combine
                 advantages from the basic features, reduce drawbacks
                 from basic features alone, and improve the detection
  notes =        "
                 EvoApplications2013 held in conjunction with
                 EuroGP2013, EvoCOP2013, EvoBio'2013 and EvoMusArt2013",

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang