A GP Artificial Ant for image processing: preliminary experiments with EASEA.

  • Enzo Bolis
  • Christian Zerbi
  • Pierre Collet
  • Jean Louchet
  • Evelyne Lutton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2038)


This paper describes how animat-based “food foraging” techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient lines with a minimum exploration time. The resulting animats do not use standard “scanning + filtering” techniques but develop other image exploration strategies close to contour tracking. Experimental results on grey level images are presented.


Fitness Function Grey Level Image Contour Detection Left Turn CMOS Image Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Enzo Bolis
    • 1
  • Christian Zerbi
    • 1
  • Pierre Collet
    • 2
  • Jean Louchet
    • 1
  • Evelyne Lutton
    • 3
  1. 1.ENSTAParis cedex 15
  2. 2.Ecole PolytechniquePalaiseau cedex
  3. 3.INRIALe Chesnay cedex

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