Evolving Visual Routines

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{johnson:1994:EVR,
  author =       "Michael Patrick Johnson and Pattie Maes and 
                 Trevor Darrell",
  title =        "Evolving Visual Routines",
  booktitle =    "ARTIFICIAL LIFE IV, Proceedings of the fourth
                 International Workshop on the Synthesis and Simulation
                 of Living Systems",
  year =         "1994",
  editor =       "Rodney A. Brooks and Pattie Maes",
  pages =        "198--209",
  address =      "MIT, Cambridge, MA, USA",
  month =        "6-8 " # jul,
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://pubs.media.mit.edu/pubs/papers/alife-iv.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/402594.html",
  abstract =     "Traditional machine vision assumes that the vision
                 system recovers a complete, labeled description of the
                 world [Marr]. Recently, several researchers have
                 criticized this model and proposed an alternative model
                 which considers perception as a distributed collection
                 of task-specific, task-driven visual routines
                 [Aloimonos, Ullman]. Some of these researchers have
                 argued that in natural living systems these visual
                 routines are the product of natural selection
                 [ramachandran]. So far, researchers have hand-coded
                 task-specific visual routines for actual
                 implementations (e.g. [Chapman]). In this paper we
                 propose an alternative approach in which visual
                 routines for simple tasks are evolved 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 [Koza]. The results obtained are promising:
                 the evolved routines are able to correctly classify up
                 to 93% of the images, which is better than the best
                 algorithm we were able to write by hand.",
  notes =        "alife-4

                 See also \cite{johnson:1994:EVRAL}",
}

Genetic Programming entries for Michael Patrick Johnson Pattie Maes Trevor Darrell

Citations