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Genetic Algorithm Based Heuristic Measure for Pattern Similarity in Kirlian Photographs

  • Mario Köppen
  • Bertram Nickolay
  • Hendrik Treugut
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2037)

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.

Keywords

Genetic Algorithm Document Image Visual Similarity Discharge Mark Pattern Part 
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

  • Mario Köppen
    • 1
  • Bertram Nickolay
    • 1
  • Hendrik Treugut
    • 2
  1. 1.Fraunhofer IPK BerlinBerlinGermany
  2. 2.Stauferklinik Schwäbisch GmündMutlangenGermany

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