Evolving Visual Routines

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Michael Patrick Johnson and Pattie Maes and 
                 Trevor Darrell",
  title =        "Evolving Visual Routines",
  journal =      "Artificial Life",
  year =         "1994",
  volume =       "1",
  number =       "4",
  pages =        "373--389",
  month =        "summer",
  keywords =     "genetic algorithms, genetic programming, active
                 vision, visual routines",
  DOI =          "doi:10.1162/artl.1994.1.4.373",
  size =         "17 pages",
  abstract =     "Traditional machine vision assumes that the vision
                 system recovers a complete, labeled description of the
                 world [10]. Recently, several researchers have
                 criticized this model and proposed an alternative model
                 that considers perception as a distributed collection
                 of task-specific, context-driven visual routines
                 [1,12]. Some of these researchers have argued that in
                 natural living systems these researchers have argued
                 that in natural selection [11]. So far, researchers
                 have hand-coded task-specific visual routines for
                 actual implementations (e.g.,[3]). In this article we
                 propose an alternative approach in which visual
                 routines for simple tasks are created using an
                 artificial evolution approach. We present results from
                 a series of runs on actual camera images, in which
                 simple routines were evolved using genetic programming
                 techniques [7]. The results obtained are promising: The
                 evolved routines are able to process correctly up to
                 93percent of the test images, which is better than any
                 algorithm we were able to write by hand.",
  notes =        "Extension of \cite{johnson:1994:EVR}",

Genetic Programming entries for Michael Patrick Johnson Pattie Maes Trevor Darrell