Unsupervised Learning for Edge Detection Using Genetic Programming

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

  title =        "Unsupervised Learning for Edge Detection Using Genetic
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  pages =        "117--124",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, Genetic programming, Evolutionary
                 Computer Vision",
  DOI =          "doi:10.1109/CEC.2014.6900444",
  abstract =     "In edge detection, a machine learning algorithm
                 generally requires training images with their ground
                 truth or designed outputs to train an edge detector.
                 Meanwhile the computational cost is heavy for most
                 supervised learning algorithms in the training stage
                 when a large set of training images is used. To learn
                 edge detectors without ground truth and reduce the
                 computational cost, an unsupervised Genetic Programming
                 (GP) system is proposed for low-level edge detection. A
                 new fitness function is developed from the energy
                 functions in active contours. The proposed GP system
                 uses single images to evolve GP edge detectors, and
                 these evolved edge detectors are used to detect edges
                 on a large set of test images. The results of the
                 experiments show that the proposed unsupervised
                 learning GP system can effectively evolve good edge
                 detectors to quickly detect edges on different natural
  notes =        "WCCI2014",

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang