Development of evolution based technology for Image Recognition Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@MastersThesis{Hovedoppgave_Jens-Petter_Sandvik,
  title =        "Development of evolution based technology for Image
                 Recognition Systems",
  author =       "Jens-Petter Skjelvag Sandvik",
  school =       "The University Of Oslo",
  year =         "2005",
  month =        "4 " # nov,
  abstract =     "A traffic sign detection system in the vehicle can be
                 of great help for the driver. The number of accidents
                 can be reduced by 20 percent if the speed limits are
                 followed. A system that warns the driver about speeding
                 could therefore save lives if the driver reduces the
                 speed. This work focus on the colour classification
                 used in traffic sign detection methods. Existing
                 methods are compared, and a Genetic Algorithm is used
                 for optimising parameters used in the existing colour
                 classification methods. Cartesian Genetic Programming
                 is used for evolving colour classifiers for traffic
                 signs, and compared to the existing methods. The
                 evolved classifier is tested with three different
                 luminance adjustment algorithms. The results show that
                 the GA is able to find better parameters than the
                 reported parameters, and some of the evolved colour
                 classifiers were better than the existing methods. The
                 CGP architecture did find better classifiers than the
                 existing. The luminance adjustment algorithms did not
                 result in better classification results.",
  bibsource =    "OAI-PMH server at wo.uio.no",
  language =     "eng",
  oai =          "oai:digbib.uio.no/32422",
  subject =      "VDP:420",
  URL =          "http://urn.nb.no/URN:NBN:no-11481",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  size =         "115 pages",
}

Genetic Programming entries for Jens-Petter Skjelvag Sandvik

Citations