Edge Detection of Petrographic Images Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Brian J. Ross and Frank Fueten and Dmytro Y. Yashkir",
  title =        "Edge Detection of Petrographic Images Using Genetic
  pages =        "658--665",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and 
                 Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.cosc.brocku.ca/~bross/research/rw047.ps",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW047.pdf",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW047.ps",
  URL =          "http://citeseer.ist.psu.edu/317285.html",
  size =         "8 pages",
  abstract =     "We discuss work in progress that uses genetic
                 programming to evolve edge detectors for petrographic
                 images. Microscopic images of thin sections from
                 mineral samples are obtained using a rotating polarizer
                 microscope. These images are then processed using a
                 number of filters, resulting in a set of nine filtered
                 image parameters. In order to be useful for
                 higher--level analysis, such as automatic mineral
                 identification, the grain boundaries within these
                 images must be identified. Using genetic programming,
                 edge detecting functions are evolved for this purpose.
                 The edge detectors may use as any of the filtered image
                 parameters as input. Since the source images are large,
                 a subset of the images is sampled for training, and the
                 remainder of the image is used for testing. This
                 training data is selected with a biased random sampling
                 strategy. The complexity of the images dictates that a
                 generic edge detector for all mineral specimens is
                 infeasible. Rather, the ...",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of

Genetic Programming entries for Brian J Ross Frank Fueten Dmytro Y Yashkir