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Design of Correlation Filters for Pattern Recognition Using a Noisy Training Image

  • Pablo M. Aguilar-González
  • Vitaly Kober
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

Correlation filters for object detection and location estimation are commonly designed assuming the shape and graylevel structure of the object of interest are explicitly available. In this work we propose the design of correlation filters when the appearance of the target is given in a single training image. The target is assumed to be embedded in a cluttered background and the image is assumed to be corrupted by additive sensor noise. The designed filters are used to detect the target in an input scene modeled by the nonoverlapping signal model. An optimal correlation filter, with respect to the peak-to-output energy ratio criterion, is proposed for object detection and location estimation. We also present estimation techniques for the required parameters. Computer simulation results obtained with the proposed filters are presented and compared with those of common correlation filters.

Keywords

correlation filters pattern recognition 

References

  1. 1.
    VanderLugt, A.: Signal detection by complex spatial filtering. IEEE Transactions on Information Theory 10(2), 139–145 (1964)CrossRefGoogle Scholar
  2. 2.
    Kumar, B.V.K.V., Mahalanobis, A., Juday, R.: Correlation pattern recognition. Cambridge University Press, Cambridge (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Kumar, B.V.K.V., Hassebrook, L.: Performance measures for correlation filters. Applied Optics 29(20), 2997–3006 (1990)CrossRefGoogle Scholar
  4. 4.
    Yaroslavsky, L.P.: The theory of optimal methods for localization of objects in pictures. In: Wolf, E. (ed.) Progress in Optics, pp. 145–201. Elsevier, Amsterdam (1993)Google Scholar
  5. 5.
    Kumar, B.V.K.V., Dickey, F.M., DeLaurentis, J.M.: Correlation filters minimizing peak location errors. Journal of the Optical Society of America A 9(5), 678–682 (1992)CrossRefGoogle Scholar
  6. 6.
    Kober, V., Campos, J.: Accuracy of location measurement of a noisy target in a nonoverlapping background. Journal of the Optical Society of America A 13(8), 1653–1666 (1996)CrossRefGoogle Scholar
  7. 7.
    Javidi, B., Wang, J.: Design of filters to detect a noisy target in nonoverlapping background noise. Journal of the Optical Society of America A 11(10), 2604–2612 (1994)CrossRefGoogle Scholar
  8. 8.
    Javidi, B., Zhang, G., Parchekani, F.: Minimum-mean-square-error filters for detecting a noisy target in background noise. Applied Optics 35, 6964–6975 (1996)CrossRefGoogle Scholar
  9. 9.
    Javidi, B.: Real-Time Optical Information Processing. Academic Press, London (1994)Google Scholar
  10. 10.
    Ramos-Michel, E.M., Kober, V.: Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes. Journal of the Optical Society of America. A 24(11), 3403–3417 (2007)CrossRefGoogle Scholar
  11. 11.
    Mahalanobis, A., VijayaKumar, B.V.K., Song, S., Sims, S.R.F., Epperson, J.F.: Unconstrained correlation filters. Applied Optics 33(17), 3751–3759 (1994)CrossRefGoogle Scholar
  12. 12.
    González-Fraga, J., Kober, V., Álvarez-Borrego, J.: Adaptive synthetic discriminant function filters for pattern recognition. Optical Engineering 45, 057005 (2006)CrossRefGoogle Scholar
  13. 13.
    Ramos-Michel, E.M., Kober, V.: Adaptive composite filters for pattern recognition in linearly degraded and noisy scenes. Optical Engineering 47, 047204 (2008)CrossRefGoogle Scholar
  14. 14.
    Aguilar-González, P.M., Kober, V.: Correlation filters for pattern recognition using a noisy reference. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. LNCS, vol. 5197, pp. 38–45. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Aguilar-González, P.M., Kober, V.: Correlation pattern recognition in nonoverlapping scene using a noisy reference. In: Bayro-Corrochano, E., Eklundh, J.-O. (eds.) CIARP 2009. LNCS, vol. 5856, pp. 555–562. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  16. 16.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys 38(4) (2006)Google Scholar
  17. 17.
    Pratt, W.K.: Digital Image Processing. John Wiley & Sons, Chichester (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

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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