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Genetic algorithm based heuristic measure for pattern similarity in kirlian photographs

 
: Köppen, M.; Nickolay, B.; Treugut, H.

:

Boers, E.J.W.:
Applications of evolutionary computing. Proceedings : EvoWorkshops 2001. EvoCOP, EvoFlight, EvoIASP, EvoLearn, and EvoSTIM, Como, Italy, April 18-20, 2001
Berlin: Springer, 2001 (Lecture Notes in Computer Science 2037)
ISBN: 3-540-41920-9
ISSN: 0302-9743
pp.317-324
European Workshop on Evolutionary Computation in Combinatorial Optimization (EvoCOP) <1, 2001, Como>
European Workshop on Evolutionary Aeronautics (EvoFlight) <2, 2001, Como>
European Workshop on Evolutionary Computation in Image Analysis and Signal Processing (EvoIASP) <3, 2001, Como>
European Workshop on Evolutionary Learning (EvoLearn) <1, 2001, Como>
European Workshop on Evolutionary Scheduling and Timetabling (EvoSTIM) <2, 2001, Como>
English
Conference Paper
Fraunhofer IPK ()

Abstract
This paper presents the use of a genetic algorithm based heuristic measure for quantifying perceptable, similarity of visual patterns by the example of Kirlian photographs. Measuring similarity of such patterns can be considered a trade-off between quantifying strong similarity for some parts of the pattern, and the neglection of the accidental abscense of other pattern parts as well. For this reason, the use of a dynamic measure instead of a static one is motivated. Due to their well-known schemata processing abilities, genetic algorithm seem to be a good choice for "performing" such a measurement. The results obtained from a real set of Kirlian images shows that the ranking of the proposed heuristic measure is able to reflect the apparent visual similarity ranking of Kirlian patterns.

: http://publica.fraunhofer.de/documents/B-90456.html