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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S.D. Kirlian and V.K. Kirlian. Photography and visual observations by means of high frequency currents. J.Sci.Appl.Photography, 6:397–403, 1964.Google Scholar
  2. 2.
    R.S. Chouhan. Towards a biophysical explanation of the coronal formations obtained in kirlian photography in relation to cancer. In Proc. of the 3rd Intl. Conf. for Medical and Applied Bioelectrography, Helsinki, Finlandia, pages 19–21, 1996.Google Scholar
  3. 3.
    L. Konikiewicz. Kirlian photography in theory and clinical applications. J.Biol.Photogr.Assoc., 45:115–134, 1997.Google Scholar
  4. 4.
    Y. Omura. Acupuncture, infra-red thermography and kirlian photography. Acupunct.Electrother.Res., 2:43–86, 1977.CrossRefGoogle Scholar
  5. 5.
    Peter Mandel. Energetische Terminalpunkt-Diagnose. Synthesis-Verlag, 1983. (in German).Google Scholar
  6. 6.
    J. Pehek, H. Kyler, and D. Faust. Image modulation in corona discharge photography. Science, 194:263–270, 1976.CrossRefGoogle Scholar
  7. 7.
    H. Treugut. Kirlian Fotographie: Reliabilität der Energetischen Terminalpunktdiagnose (ETD) nach Mandel bei gesunden Probanden. Forsch.Komplementärmed., 4:210–217, 1997. (in German).CrossRefGoogle Scholar
  8. 8.
    H. Treugut. Kirlian Fotographie: Reliabilität der Energetischen Terminalpunktdiagnose (ETD) nach Mandel bei Kranken. Forsch.Komplementärmed., 5:224–229, 1998. (in German).CrossRefGoogle Scholar
  9. 9.
    John H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975.Google Scholar
  10. 10.
    David E. Goldberg. Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley, Reading MA, 1989.zbMATHGoogle Scholar
  11. 11.
    Shumee Baluja. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, 1994.Google Scholar
  12. 12.
    Chris Stephens and Henri Waelbroeck. Schemata evolution and building blocks. Evolutionary Computation, 7:109–124, 1999.CrossRefGoogle Scholar
  13. 13.
    Mario Köppen, Dörte Waldöstl, and Bertram Nickolay. A system for the automated evaluation of invoices. In Jonathan H. Hull and Suzanne L. Taylor, editors, Document Analysis Systems II, pages 223–241. World Scientific, Singapore a.o., 1997.Google Scholar

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

Personalised recommendations