Edge Detector Evolution using Multidimensional Multiobjective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Yang Zhang and Peter I. Rockett",
  title =        "Edge Detector Evolution using Multidimensional
                 Multiobjective Genetic Programming",
  howpublished = "citeseerx",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/download?doi=",
  size =         "25 pages",
  abstract =     "In this paper we report the evolution of a feature
                 extraction stage for edge detection using
                 multidimensional multiobjective genetic programming. We
                 have employed training and validation data produced
                 using a realistic model of the imaging physics to
                 evolve an n2-to-m mapping which projects the pixel
                 intensities of an n by n image patch into an
                 m-dimensional decision space. The (near-)optimal value
                 of m is also simultaneously determined during
                 evolution. A conventional Fisher linear discriminant is
                 then used to classify edge patterns. On the independent
                 validation set, the suggested edge detector is shown to
                 give performance superior to both the well-known
                 conventional Canny detector and to earlier
                 multi-objective genetic programming results which
                 projected the pattern vector into a one-dimensional
                 decision space. In addition, the superiority of the new
                 detector is also demonstrated on a hand-labeled set of
                 real images.",
  notes =        "See \cite{Zhang:2009:EC}",

Genetic Programming entries for Yang Zhang Peter I Rockett