Is a Single Image Sufficient for Evolving Edge Features by Genetic Programming?

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

@InProceedings{Fu:evoapps14,
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Is a Single Image Sufficient for Evolving Edge
                 Features by Genetic Programming?",
  booktitle =    "17th European Conference on the Applications of
                 Evolutionary Computation",
  year =         "2014",
  editor =       "Anna Isabel Esparcia-Alcazar and Antonio Miguel Mora",
  series =       "LNCS",
  volume =       "8602",
  publisher =    "Springer",
  pages =        "451--463",
  address =      "Granada",
  month =        "23-25 " # apr,
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Edge
                 Detection; Gaussian Filter",
  isbn13 =       "978-3-662-45522-7",
  DOI =          "doi:10.1007/978-3-662-45523-4_37",
  abstract =     "Typically, a single natural image is not sufficient to
                 train a program to extract edge features in edge
                 detection when only training images and their ground
                 truth are provided. However, a single training image
                 might be considered as proper training data when domain
                 knowledge, such as used in Gaussian-based edge
                 detection, is provided. In this paper, we employ
                 Genetic Programming (GP) to automatically evolve
                 Gaussian-based edge detectors to extract edge features
                 based on training data consisting of a single image
                 only. The results show that a single image with a high
                 proportion of true edge points can be used to train
                 edge detectors which are not significantly different
                 from rotation invariant surround suppression. When the
                 programs separately evolved from eight single images
                 are considered as weak classifiers, the combinations of
                 these programs perform better than rotation invariant
                 surround suppression.",
  notes =        "EvoApplications2014 held in conjunction with
                 EuroGP'2014, EvoCOP2014, EvoBIO2014, and
                 EvoMusArt2014",
}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations