Correlation Filters for Pattern Recognition Using a Noisy Reference

  • Pablo M. Aguilar-González
  • Vitaly Kober
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)


Correlation filters for recognition of a target in overlapping background noise are proposed. The object to be recognized is given implicitly; that is, it is placed in a noisy reference image at unknown coordinates. For the filters design two performance criteria are used: signal-to-noise ratio and peak-to-output energy. Computer simulations results obtained with the proposed filters are discussed and compared with those of classical correlation filters in terms of discrimination capability.


correlation filters pattern recognition 


  1. 1.
    VanderLugt, A.B.: Signal Detection by Complex Filtering. IEEE Trans. Inf. Theory IT 10, 139–145 (1964)CrossRefGoogle Scholar
  2. 2.
    Vijaya-Kumar, B.V.K., Mahalanobis, A., Juday, R.D.: Correlation Pattern Recognition. Cambridge U. Press, Cambridge (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Vijaya-Kumar, B.V.K., Hassebrook, L.: Performance Measures for Correlation Filters. Appl. Opt. 29, 2997–3006 (1990)CrossRefGoogle Scholar
  4. 4.
    Horner, J.L., Gianino, P.D.: Phase-Only Matched Filtering. Appl. Opt. 23, 812–816 (1984)CrossRefGoogle Scholar
  5. 5.
    Yaroslavsky, L.P.: The Theory of Optimal Methods for Localization of Objects in Pictures. In: Wolf, E. (ed.) Progress in Optics, vol. 33, pp. 145–201. Elsevier, Amsterdam (1993)Google Scholar
  6. 6.
    Javidi, B., Wang, L.: Optimum Filter for Detection of a Target in Nonoverlapping Scene Noise. Appl. Opt. 33, 4454–4458 (1994)CrossRefGoogle Scholar
  7. 7.
    Javidi, B., Wang, J.: Design of Filters to Detect a Noisy Target in Nonoverlapping Background Noise. J. Opt. Soc. Am. A 11, 2604–2612 (1994)CrossRefGoogle Scholar
  8. 8.
    Réfrégier, P., Javidi, B., Zhang, G.: Minimum Mean Square Error Filter for Pattern Recognition With Spatially Disjoint Signal and Scene Noise. Opt. 18, 1453–1455 (1993)Google Scholar
  9. 9.
    Javidi, B., Horner, J.L.: Real-Time Optical Information Processing. Academic Press, Inc., London (1994)Google Scholar
  10. 10.
    Vijaya-Kumar, B.V.K., Dickey, F.M., DeLaurentis, J.M.: Correlation Filters Minimizing Peak Location Errors. J. Opt. Soc. Am. A 9, 678–682 (1992)CrossRefGoogle Scholar
  11. 11.
    Kober, V., Campos, J.: Accuracy of Location Measurement of a Noisy Target in a Nonoverlapping Background. Journal OSA 13, 1653–1666 (1996)Google Scholar
  12. 12.
    Kober, V., Ovseyevich, A.: Phase-Only Filter with Improved Filter Efficiency and Correlation Discrimination. Pattern Recognition and Image Analysis 10, 514–519 (2000)Google Scholar
  13. 13.
    Diaz-Ramirez, V.H., Kober, V., Álvarez-Borrego, J.: Pattern Recognition With an Adaptive Joint Transform Correlator. Appl. Opt. 45, 5929–5941 (2006)CrossRefGoogle Scholar
  14. 14.
    González-Fraga, J.A., Kober, V., Álvarez-Borrego, J.: Adaptive Synthetic Discriminant Function Filters for Pattern Recognition. Opt. Eng. 45, 057005, 1–10 (2006)Google Scholar
  15. 15.
    Ramos, E.M., Kober, V.: Design of Correlation Filters for Recognition of Linearly Distorted Objects in Linearly Degraded Scenes. J. Opt. Soc. Am. A 24, 3403–3417 (2007)CrossRefGoogle Scholar
  16. 16.
    Jenkins, J.M., Watts, D.G.: Spectral Analysis and its Applications. Holden-Day, Inc. (1968)Google Scholar
  17. 17.
    Dougherty, E.R.: Random Processes for Image and Signal Processing. Wiley-IEEE Press (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Pablo M. Aguilar-González
    • 1
  • Vitaly Kober
    • 1
  1. 1.Department of Computer ScienceCentro de Investigación Científica y de Educación Superior de EnsenadaEnsenadaMéxico

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