Target Locating in Clutter

  • Leonid Yaroslavsky


In this chapter we discuss the problem of locating targets in images that contain, besides the target object, a clutter of non-target objects that obscure the target object. As it follows from the discussion in Sect. 10.7, background non-target objects represent the main obstacle for reliable object localization in this case. Our purpose therefore is to find out how can one design a localization device that minimizes the danger of false identification of the target object with one of the non-target objects.


Power Spectrum Input Image Target Object Background Component Localization Device 
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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  1. 1.
    L. Schwartz, “Analyse Mathematique”, Hermann, Paris, 1967MATHGoogle Scholar
  2. 2.
    L. Yaroslaysky, “Adaptive Nonlinear Correlators for Object location and Recognition”, In: Advanced Optical Correlators for Pattern Recognition and Association, Ed. Kazuyoshi Itoh,Research Signpost, Trivandrum, Kerala, State, India, 1997, pp. 47–61.Google Scholar
  3. 3.
    L. Yaroslaysky, “Nonlinear Optical Correlators with improved discrimination Capability for Object Location and Pattern Recognition”, In: Optical Patern Recognition, Ed. F.S.T. Yu and S. Jutamulia, Cambridge University Press, 1998, 141–170Google Scholar
  4. 4.
    R.B. Blackman and J.W. Tukey, The Measurement of Power Spectra, New York: Dover, 1958Google Scholar
  5. 5.
    L. Yaroslaysky, O. Lopez-Coronado, Juan Campos, Input image “homogenization” method for improvement the correlator’s discrimination capability, Optics Letters, 15 July, 1998Google Scholar
  6. 6.
    D. Marr, Vision. A Computational Investigation into the Human Representation and Processing of Visual Information, W.H. Freeman and Co., N.Y. 1982Google Scholar
  7. 7.
    A. Vander Lugt, Signal Detection by Complex Spatial Filtering, IEEE Trans., IT-10, 1964, No. 2, p. 139Google Scholar
  8. 8.
    S. Lowenthal, Y. Belvaux, Reconnaissance des Formes par Filtrage des Frequences Spaciales, Optica Acta, v. 14, p. 245–258, 1967MathSciNetCrossRefGoogle Scholar
  9. 9.
    A. Vander Lugt, A Review of Optical Data-Processing Techniques, Optica Acta, v. 15, 1–33, 1968ADSCrossRefGoogle Scholar
  10. 10.
    R. A. Binns, A. Dickinson, B. M. Watrasiewicz, Method of Increasing Discrimination of Optical Filtering, Appl. Optics, v. 7, 1047–1051, 1968ADSCrossRefGoogle Scholar
  11. 11.
    J. L. Horner, P.D. Gianino, Phase-only Matched Filtering, Appl. Opt., v. 23, p. 812–816, 1984Google Scholar
  12. 12.
    J.L Horner, J. R. Leger, Pattern Recognition with Binary Phase-only Filters, Appl. Opt., v. 24, 609–611, 1985Google Scholar
  13. 13.
    G.-G. Mu, X.-M. Wang, Z.-O. Wang, Appl. Opt., v. 27, 34–61, 1988Google Scholar
  14. 14.
    A.A. S. Awwal, M. A. Karim, S. R. Jahan, Improved Correlation Discrimination Using an Amplitude-modulated Phase-only Filter, Appl. Opt., v. 29, 233–236, 1990Google Scholar
  15. 15.
    B. V. K. Vijaya Kumar, Z. Bahri, Phase-only Filters with Improved Signal to Noise Ratio, Appl. Opt., 28, pp. 250–257, 1989Google Scholar
  16. 16.
    F. M. Dickey, B. V. K. Vijaya Kumar, L. A. Romero, J. M. Connelly, Complex Ternary Matched Filters Yielding High Signal to Noise Ration, Opt. Eng., v. 29, p. 994–1001, 1990ADSCrossRefGoogle Scholar
  17. 17.
    W. W. Farn, J. W. Goodman, Optimal Binary Phase-only Matched Filters, Appl. Opt., v. 27, pp. 4431–4437, 1988Google Scholar
  18. 18.
    A. Mahalanobis, B. V. K. Vijaya Kumar, D. Casasent, Minimum Average Correlation Energy Filters, Appl. Opt., v. 26, pp. 3633–3640, 1987Google Scholar
  19. 19.
    M. Fleisher, U. Mahlab, J. Shamir, Entropy Optimized Filter for Pattern Recognition, Appl. Opt. V. 29, pp. 2091–2098, 1990ADSCrossRefGoogle Scholar
  20. 20.
    K. Chalasinska-Macukow, “Generalized Matched Spatial Filters with Optimum Light Efficiency”, in Optical Processing and Computing, H. H. Arsenault, T. Szoplik and B. Macukow, Ed. ( Academic Press, Boston, 1989 ), p. 31–45.Google Scholar
  21. 21.
    L.P. Yaroslaysky, “Optical Correlators with (-k)th Law Nonlinearity: Optimal and Suboptimal Solutions”, Applied Optics, 34, pp. 3924–3932 (1995)ADSCrossRefGoogle Scholar
  22. 22.
    L. Yaroslaysky, E. Marom, Nonlinearity Optimization in Nonlinear Joint Transform Correlators, Applied Optics, vol. 36, No. 20, 10 July, 1997, pp. 4816–4822Google Scholar
  23. 23.
    L. Yaroslaysky, Optimal Target Location in Color and Multi-component Images, Asian Journal of Physics, Vol. 8, No 3, 1999, pp. 100–113Google Scholar
  24. 24.
    K. Yamaba and Y. Miyake, Yamaba and Y. Miyake, “Color character recognition method based on human perception,” Opt. Eng. 32, 33–40 (1993).ADSCrossRefGoogle Scholar
  25. 25.
    S.L. Guth, “Model for color vision and light adaptation,” J. Opt. Soc. Am. 8, 976–993 (1991).ADSCrossRefGoogle Scholar
  26. 26.
    E. Badique, Y. Komiya, N. Ohyama, J. Tsujiuchi and T. Honda, “Color image correlation” Optics Comm. 62, 181–186, (1987).ADSCrossRefGoogle Scholar
  27. 27.
    E.Badique, N.Ohyama, T.Honda and J.Tsujiuchi, “Color image correlation for spatial/spectral recognition and increased selectivity” Optics Comm. 68, 91–96, (1988).ADSCrossRefGoogle Scholar
  28. 28.
    V. Kober, V. Lashin, L. Moreno, J. Campos, L. Yaroslaysky, and M.J. Yzuel, Color component transformations for optical pattern recognition, JOSA, A/vol. 14, No. 10, October 1997, pp. 2656–2669CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2004

Authors and Affiliations

  • Leonid Yaroslavsky
    • 1
  1. 1.Tel Aviv UniversityIsrael

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