Object recognition using multidimensional receptive field histograms

  • Bernt Schiele
  • James L. Crowley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1064)


This paper presents a technique to determine the identity of objects in a scene using histograms of the responses of a vector of local linear neighborhood operators (receptive fields). This technique can be used to determine the most probable objects in a scene, independent of the object's position, image-plane orientation and scale. In this paper we describe the mathematical foundations of the technique and present the results of experiments which compare robustness and recognition rates for different local neighborhood operators and histogram similarity measurements.


Receptive Field Object Recognition Recognition Rate Color Histogram Gabor Filter 
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.


  1. 1.
    J. G. Daugman. High confidence visual recognition of persons by test of statistical independance. IEEE PAMI, 15(11):1148–1161, November 1993.Google Scholar
  2. 2.
    F. Ennesser and G. Medioni. Finding waldo, or focus of attention using local color information. IEEE PAMI, 17(8):805–809, 1995.Google Scholar
  3. 3.
    L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, and M. A. Viergever. General intensity tranformations and second order invariants. In SCIA '91, 1991.Google Scholar
  4. 4.
    W. T. Freeman and E. H. Adelson. The design and use of steerable filters. IEEE PAMI, 13(9):891–906, 1991.Google Scholar
  5. 5.
    B. V. Funt and G. D. Finlayson. Color constant color indexing. IEEE PAMI, 17(5):522–529, 1995.Google Scholar
  6. 6.
    G. Healey and D. Slater. Using illumination invariant color histogram descriptors for recognition. In CVPR, pages 355–360, 1994.Google Scholar
  7. 7.
    W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C. Cambridge University Press, 2nd edition, 1992.Google Scholar
  8. 8.
    B. Schiele and J. L. Crowley. The robustness of object recognition to rotation using multidimensional receptive field histograms. submitted to International Workshop on Object Representation for Computer Vision, April 1996.Google Scholar
  9. 9.
    B. Schiele and J. L. Crowley. Probabilistic object recognition using multidimensional receptive field histograms. submitted to ICPR'96, August 1996.Google Scholar
  10. 10.
    M.J. Swain and D.H. Ballard. Color indexing. IJCV, 7(1):11–32, 1991.Google Scholar
  11. 11.
    C.-J. Westelius. Preattentive Gaze Control for Robot Vision. PhD thesis, Department of Electrical Engineering, Linköping University, 1992.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Bernt Schiele
    • 1
  • James L. Crowley
    • 1
  1. 1.LIFIA/GRAVIRGrenobleFrance

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