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A Statistical Matrix Representation Using Sliced Orthogonal Nonlinear Correlations for Pattern Recognition

  • P. García-Martínez
  • H. H. Arsenault
  • C. Ferreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1876)

Abstract

In pattern recognition, the choice of features to be detected is a critical factor to determine the success or failure of a method; much research has gone into finding the best features for particular tasks [1]. When images are detected by digital cameras, they are usually acquired as rectangular arrays of pixels, so the initial features are pixel values. Some methods use those pixel values directly for processing, for instance in normal matched filtering [2], whereas other methods execute some degree of pre-processing, such as binarizing the pixel values [3].

Keywords

Gray Level Reference Object Counting Operation Discrimination Capability Noise Robustness 
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]
    R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, (1973). Chapter. 1.zbMATHGoogle Scholar
  2. [2]
    A. Vander Lugt, A. VanderLugt, “ IEEE Trans. Inf. Theory, IT-10, 139–145 (1964).CrossRefGoogle Scholar
  3. [3]
    B. V. K. V. Kumar and D. Casasent, Appl. Opt. 20, 143–147, (1980).Google Scholar
  4. [4]
    P. Garcia-Martinez and H. H. Arsenault, Opt. Commn. 172, 181–192 (1999).CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • P. García-Martínez
    • 1
    • 2
  • H. H. Arsenault
    • 1
  • C. Ferreira
    • 2
  1. 1.COPLUniversite LavalSte-FoyCanada
  2. 2.Dpt. D’ÒpticaUniversität de ValènciaBurjassotSpain

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