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Stable Feature Extraction with the Help of Stochastic Information Measure

  • Alexander Lepskiy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

This article discusses the problem of extraction of such set of pattern features that is informative and stable with respect of stochastic noise. This is done through the stochastic information measure.

Keywords

Additive Measure Information Measure Stochastic Noise Discrete Curve Stochastic Measure 
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.

References

  1. 1.
    Bronevich, A., Lepskiy, A.: Geometrical Fuzzy Measures in Image Processing and Pattern Recognition. In: Proc. of the 10th IFSA World Congress, Istanbul, Turkey, pp. 151–154 (2003)Google Scholar
  2. 2.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification and Scene Analysis: Part I Pattern Classification. John Wiley and Sons, Chichester (1998)Google Scholar
  3. 3.
    Lepskii, A.E.: On Stability of the Center of Masses of the Vector Representation in One Probabilistic Model of Noiseness of an Image Contour. Automation and Remote Control 68, 75–84 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Lepskiy, A.E.: Application of Stochastic Information Measure in Problem of Finding of Optimal Polygonal Curve Representation. In: Proc. of Intern. Conf. Pattern Recognition and Image Analysis, Nizhni Novgorod, vol. 1, pp. 397–400 (2008)Google Scholar
  5. 5.
    Shiryaev, A.N.: Probability (Graduate Texts in Mathematics). Springer, Heidelberg (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexander Lepskiy
    • 1
  1. 1.Higher School of EconomicsMoscowRussia

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