Pattern Recognition and Image Analysis

, Volume 16, Issue 3, pp 425–431

Adaptive correlation filters for pattern recognition

  • V. Kober
  • M. Mozerov
  • I. A. Ovseevich
Image Processing, Analysis, Recognition, and Understanding

Abstract

Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance.

Keywords

pattern recognition correlation filters adaptive filters 

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References

  1. 1.
    A. B. Vander Lugt, “Signal Detection by Complex Filtering,” IEEE Trans. on Inf. Theory 10(6), 139–135 (1964).CrossRefGoogle Scholar
  2. 2.
    J. L. Horner and P. D. Gianino, “Phase-Only Matched Filtering,” Applied Optics 23, 812–816 (1984).Google Scholar
  3. 3.
    D. Casasent, “Unified Synthetic Discriminant Function Computational Formulation,” Applied Optics 23(10), 1620–1627 (1984).Google Scholar
  4. 4.
    H. Arsenault and Y. Hsu, “Rotation Invariant Discrimination between Almost Similar Objects,” Applied Optics 22, 130–132 (1983).Google Scholar
  5. 5.
    A. Mahalanobis, B. V. K. Vijaya Kumar, and D. Casasent, “Minimum Average Correlation Filters,” Applied Optics 26, 3633–3640 (1987).CrossRefGoogle Scholar
  6. 6.
    B. V. K. Vijaya Kumar and L. Hassebrook, “Performance Measures for Correlation Filters,” Applied Optics 29(20), 2997–3006 (1990).Google Scholar
  7. 7.
    B. V. K. Vijaya Kumar, “Tutorial Survey of Composite Filter Designs for Optical Correlators,” Applied Optics 31(23), 4773–4801 (1992).Google Scholar
  8. 8.
    L. P. Yaroslavsky, in The Theory of Optimal Methods for Localization of Objects in Pictures, Ed. by E. Wolf (Optics XXXII, Elsevier, 1993), Vol. 145–201.Google Scholar
  9. 9.
    V. Kober, L. P. Yaroslavsky, J. Campos, and M. J. Yzuel, “Optimal Filter Approximation by Means of a Phase Only Filter with Quantization,” Optics Letters 19(13), 978–980 (1994).CrossRefGoogle Scholar
  10. 10.
    B. Javidi and J. Wang, “Design of Filters to Detect a Noisy Target in Nonoverlapping Background Noise,” Journal OSA (A) 11, 2604–2612 (1994).Google Scholar
  11. 11.
    V. Kober and J. Campos, “Accuracy of Location Measurement of a Noisy Target in a Nonoverlapping Background,” Journal OSA (A) 13, 1653–1666 (1996).Google Scholar
  12. 12.
    V. Kober and I. A. Ovseyevich, “Phase-Only Filter with Improved Filter Efficiency and Correlation Discrimination,” Pattern Recognition and Image Analysis 10(4), 514–519 (2000).Google Scholar
  13. 13.
    V. Kober and T. S. Choi, “Single-Output Multichannel Pattern Recognition with Projection Preprocessing,” Optical Engineering 39(8), 2153–2162 (2000).CrossRefGoogle Scholar
  14. 14.
    O. Billet and L. Singher, “Adaptive Multiple Filtering,” Optical Engineering 41(1), 55–68 (2002).CrossRefGoogle Scholar
  15. 15.
    M. Jedynski and K. Chalasinska-Macukow, “Wavelet Transform for Preprocessing in an Optical Correlator with Multilevel Composite Filter,” Optical Engineering 43(8), 1759–1766 (2005).CrossRefGoogle Scholar
  16. 16.
    I. Moreno, J. Campos, M. J. Yzuel, and V. Kober, “Implementation of Bipolar Real-Valued Input Scenes in a Real-Time Optical Correlator: Application to Color Pattern Recognition,” Optical Engineering 37(1), 144–150 (1998).CrossRefGoogle Scholar
  17. 17.
    A. K. Jain, Fundamentals of Digital Image Processing (Prentice Hall, New York, 1989).MATHGoogle Scholar

Copyright information

© Pleiades Publishing, Inc. 2006

Authors and Affiliations

  • V. Kober
    • 1
    • 2
  • M. Mozerov
    • 2
  • I. A. Ovseevich
    • 2
  1. 1.Department of Computer ScienceDivision of Applied Physics CICESEEnsenadaMexico
  2. 2.Institute for Information Transmissions Problems of RAS MoscowMoscowRussia

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