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Pattern Recognition and Image Analysis

, Volume 18, Issue 4, pp 613–620 | Cite as

Adaptive nonlinear composite filters for pattern recognition

  • Saul Martínez-Diaz
  • Vitaly Kober
  • I. A. Ovseyevich
Representation, Processing, Analysis, and Understanding of Images
  • 62 Downloads

Abstract

Adaptive composite nonlinear filters for reliable illumination-invariant pattern recognition are proposed. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given value of discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in test nonuniform illuminated scenes are discussed and compared with those of linear composite correlation filters in terms of recognition performance.

Keywords

Training Image Mean Absolute Error Impulsive Noise Nonlinear Correlation Discrimination Capability 
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

© Pleiades Publishing, Ltd. 2008

Authors and Affiliations

  • Saul Martínez-Diaz
    • 1
  • Vitaly Kober
    • 1
    • 2
  • I. A. Ovseyevich
    • 2
  1. 1.Department of Computer ScienceDivision of Applied Physics, CICESEEnsenadaMexico
  2. 2.Institute for Information Transmissions Problems of RASMoscowRussia

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