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Comparison of Evolving Uniform, Non-uniform Cellular Automaton, and Genetic Programming for Centroid Detection with Hardware Agents

  • Marcus Komann
  • Andreas Mainka
  • Dietmar Fey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4671)

Abstract

Current industrial applications require fast and robust image processing in systems with low size and power dissipation. One of the main tasks in industrial vision is fast detection of centroids of objects. This paper compares three different approaches for finding geometric algorithms for centroid detection which are appropriate for a fine-grained parallel hardware architecture in an embedded vision chip. The algorithms shall comprise emergent capabilities and high problem-specific functionality without requiring large amounts of states or memory. For that problem, we consider uniform and non-uniform cellular automata (CA) as well as Genetic Programming. Due to the inherent complexity of the problem, an evolutionary approach is applied. The appropriateness of these approaches for centroid detection is discussed.

Keywords

Genetic Program Cellular Automaton Cellular Automaton Output Image Processor Element 
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 2007

Authors and Affiliations

  • Marcus Komann
    • 1
  • Andreas Mainka
    • 1
  • Dietmar Fey
    • 1
  1. 1.Institute of Computer Science, University of Jena, Ernst-Abbe-Platz 2, 07743 JenaGermany

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