Disparity Map Estimation by Combining Cost Volume Measures Using Genetic Programming

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

  author =       "Enrique Naredo and Enrique Dunn and 
                 Leonardo Trujillo",
  title =        "Disparity Map Estimation by Combining Cost Volume
                 Measures Using Genetic Programming",
  booktitle =    "EVOLVE - A Bridge between Probability, Set Oriented
                 Numerics, and Evolutionary Computation {II}",
  year =         "2012",
  editor =       "Oliver Schuetze and Carlos A. {Coello Coello} and 
                 Alexandru-Adrian Tantar and Emilia Tantar and 
                 Pascal Bouvry and Pierre {Del Moral} and Pierrick Legrand",
  volume =       "175",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "71--86",
  address =      "Mexico City, Mexico",
  month =        aug # " 7-9",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-31519-0",
  DOI =          "doi:10.1007/978-3-642-31519-0_5",
  abstract =     "Stereo vision is one of the most active research areas
                 in modern computer vision. The objective is to recover
                 3-D depth information from a pair of 2-D images that
                 capture the same scene. This paper addresses the
                 problem of dense stereo correspondence, where the goal
                 is to determine which image pixels in both images are
                 projections of the same 3-D point from the observed
                 scene. The proposal in this work is to build a
                 non-linear operator that combines three well known
                 methods to derive a correspondence measure that allows
                 us to retrieve a better approximation of the ground
                 truth disparity of stereo image pair. To achieve this,
                 the problem is posed as a search and optimisation task
                 and solved with genetic programming (GP), an
                 evolutionary paradigm for automatic program induction.
                 Experimental results on well known benchmark problems
                 show that the combined correspondence measure produced
                 by GP outperforms each standard method, based on the
                 mean error and the percentage of bad pixels. In
                 conclusion, this paper shows that GP can be used to
                 build composite correspondence algorithms that exhibit
                 a strong performance on standard tests.",
  notes =        "EVOLVE-2012",
  affiliation =  "Doctorado en Ciencias de la Ingenieria, Departamento
                 de Ingenieria Electrica y Electronica, Instituto
                 Tecnologico de Tijuana, Blvd. Industrial y Av. ITR
                 Tijuana S/N, Mesa Otay, C.P. 22500 Tijuana, B.C.,

Genetic Programming entries for Enrique Naredo Enrique Dunn Leonardo Trujillo