Detecting motion from noisy scenes using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Detecting motion from noisy scenes using Genetic
  author =       "Brian Pinto and Andy Song",
  year =         "2009",
  pages =        "322--327",
  booktitle =    "Proceeding of the 24th International Conference Image
                 and Vision Computing New Zealand, IVCNZ '09",
  month =        "23-25 " # nov,
  address =      "Wellington",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-4697-1",
  ISSN =         "2151-2205",
  DOI =          "doi:10.1109/IVCNZ.2009.5378389",
  abstract =     "A machine learning approach is presented in this study
                 to automatically construct motion detection programs.
                 These programs are generated by Genetic Programming
                 (GP), an evolutionary algorithm. They detect motion of
                 interest from noisy data when there is no prior
                 knowledge of the noise. Programs can also be trained
                 with noisy data to handle noise of higher levels.
                 Furthermore, these auto-generated programs can handle
                 unseen variations in the scene such as different
                 weather conditions and even camera movements.",
  notes =        "Also known as \cite{5378389}",

Genetic Programming entries for Brian Pinto Andy Song