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Erschienen in:
2007 | OriginalPaper | Buchkapitel
Comparison of Evolving Uniform, Non-uniform Cellular Automaton, and Genetic Programming for Centroid Detection with Hardware Agents
verfasst von : Marcus Komann, Andreas Mainka, Dietmar Fey
Erschienen in: Parallel Computing Technologies
Verlag: Springer Berlin Heidelberg
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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
evolution
ary approach is applied. The appropriateness of these approaches for centroid detection is discussed.