Low-Level Feature Extraction for Edge Detection Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Fu:2014:ieeec,
  author =       "Wenlong Fu and Mark Johnston and Mengjie Zhang",
  title =        "Low-Level Feature Extraction for Edge Detection Using
                 Genetic Programming",
  journal =      "IEEE Transactions on Cybernetics",
  year =         "2014",
  volume =       "44",
  number =       "8",
  month =        "1459--1472",
  keywords =     "genetic algorithms, genetic programming, Accuracy,
                 Detectors, Educational institutions, Feature
                 extraction, Image edge detection, Noise, Training, Edge
                 detection, feature extraction",
  ISSN =         "2168-2267",
  DOI =          "doi:10.1109/TCYB.2013.2286611",
  size =         "14 pages",
  abstract =     "Edge detection is a subjective task. Traditionally, a
                 moving window approach is used, but the window size in
                 edge detection is a tradeoff between localisation
                 accuracy and noise rejection. An automatic technique
                 for searching a discriminated pixel's neighbours to
                 construct new edge detectors is appealing to satisfy
                 different tasks. In this paper, we propose a genetic
                 programming (GP) system to automatically search pixels
                 (a discriminated pixel and its neighbours) to construct
                 new low-level subjective edge detectors for detecting
                 edges in natural images, and analyse the pixels
                 selected by the GP edge detectors. Automatically
                 searching pixels avoids the problem of blurring edges
                 from a large window and noise influence from a small
                 window. Linear and second-order filters are constructed
                 from the pixels with high occurrences in these GP edge
                 detectors. The experiment results show that the
                 proposed GP system has good performance. A comparison
                 between the filters with the pixels selected by GP and
                 all pixels in a fixed window indicates that the set of
                 pixels selected by GP is compact but sufficiently rich
                 to construct good edge detectors.",
  notes =        "also known as \cite{6649981}",
}

Genetic Programming entries for Wenlong Fu Mark Johnston Mengjie Zhang

Citations