Advertisement

Pattern Recognition by Invariant Reference Points

  • Krystian Ignasiak
  • Władysław Skarbek
Conference paper
  • 571 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1424)

Abstract

New methodology for pattern recognition is presented which is based on design of invariant reference points. It is shown that the k-NN distance classifier is a special case of this methodology. New classifiers within this framework are also described.

Keywords

Reference Point Feature Vector Selection Technique Measurement Vector Target Domain 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    C. Bishop (1995) Neural Networks for Pattern Recognition, Clarendon Press, Oxford.Google Scholar
  2. 2.
    K.I. Diamantaras, S.Y. Kung (1996) Principal Component Neural Networks, John Wiley & Sons, New York.zbMATHGoogle Scholar
  3. 3.
    R.O. Duda, P.E. Hart (1973) Pattern Classification and Scene Analysis, Wiley, New York.zbMATHGoogle Scholar
  4. 4.
    Y. Fisher, ed. (1995) Fractal Image Compression — Theory and Application, Springer Verlag.Google Scholar
  5. 5.
    S. Haykin (1994) Neural networks — A Comprehensive Foundation, Maxwell Macmillan International.Google Scholar
  6. 6.
    H. Hotelling (1933) Analysis of a complex of statistical variables into principal components, Journal of Educational Psychology 24, 417–441.CrossRefGoogle Scholar
  7. 7.
    A. Jacquin (1992) Image coding based on a fractal theory of iterated contractive image transformations, IEEE Transactions on Image Processing, 1, 18–30.CrossRefGoogle Scholar
  8. 8.
    T. Kohonen (1995) Self-Organizing Maps, Springer, Berlin.Google Scholar
  9. 9.
    Y. Linde, A. Buzo, R.M. Gray (1980) An algorithm for vector quantizer design, IEEE Trans. Comm., COM-28 1980 28–45.Google Scholar
  10. 10.
    E. Oja (1983) Subspace methods of pattern recognition, Research Studies Press, England.Google Scholar
  11. 11.
    B.D. Ripley (1996) Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge.zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Krystian Ignasiak
    • 1
  • Władysław Skarbek
    • 1
  1. 1.Department of Electronics and Information TechnologyWarsaw University of TechnologyWarsaw

Personalised recommendations