Evolving boundary detectors for natural images via Genetic Programming

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

  author =       "Ilan Kadar and Ohad Ben-Shahar and Moshe Sipper",
  title =        "Evolving boundary detectors for natural images via
                 Genetic Programming",
  booktitle =    "19th International Conference on Pattern Recognition,
                 ICPR 2008",
  year =         "2008",
  month =        dec # " 8-11",
  pages =        "1--4",
  address =      "Tampa, Florida, USA",
  keywords =     "genetic algorithms, genetic programming, computer
                 vision, learning (artificial intelligence), boundary
                 detection, boundary detectors, computer vision, filter
                 kernels, human visual system, human-marked boundaries,
                 human-marked boundary maps, learning approach, learning
                 framework, natural images, primate visual system",
  isbn13 =       "978-1-4244-2175-6",
  DOI =          "doi:10.1109/ICPR.2008.4761581",
  abstract =     "Boundary detection constitutes a crucial step in many
                 computer vision tasks. We present a novel learning
                 approach to automatically construct a boundary detector
                 for natural images via Genetic Programming (GP). Our
                 approach aims to use GP as a learning framework for
                 evolving computer programs that are evaluated against
                 human-marked boundary maps, in order to accurately
                 detect and localize boundaries in natural images. Our
                 GP system is unique in that it combines filter kernels
                 that were inspired by models of processing in the early
                 stages of the primate visual system, but makes no
                 assumption about what constitutes a boundary, thus
                 avoiding the need to make ad-hoc intuitive definitions.
                 By testing the evolved boundary detectors on a
                 benchmark set of natural images with associated
                 human-marked boundaries, we show performance to be
                 quantitatively competitive with existing
                 computer-vision approaches. Moreover, we show that our
                 evolved detector provides insights into the mechanisms
                 underlying boundary detection in the human visual
  notes =        "Also known as \cite{4761581}",

Genetic Programming entries for Ilan Kadar Ohad Ben-Shahar Moshe Sipper