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Whitening for Photometric Comparison of Smooth Surfaces under Varying Illumination

  • Margarita Osadchy
  • Michael Lindenbaum
  • David Jacobs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3024)

Abstract

We consider the problem of image comparison in order to match smooth surfaces under varying illumination. In a smooth surface nearby surface normals are highly correlated. We model such surfaces as Gaussian processes and derive the resulting statistical characterization of the corresponding images. Supported by this model, we treat the difference between two images, associated with the same surface and different lighting, as colored Gaussian noise, and use the whitening tool from signal detection theory to construct a measure of difference between such images. This also improves comparisons by accentuating the differences between images of different surfaces. At the same time, we prove that no linear filter, including ours, can produce lighting insensitive image comparisons. While our Gaussian assumption is a simplification, the resulting measure functions well for both synthetic and real smooth objects. Thus we improve upon methods for matching images of smooth objects, while providing insight into the performance of such methods. Much prior work has focused on image comparison methods appropriate for highly curved surfaces. We combine our method with one of these, and demonstrate high performance on rough and smooth objects.

Keywords

Reference Image Query Image Synthetic Image Lighting Direction Lighting Change 
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.

References

  1. 1.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class-specific linear projection. PAMI 19(7), 711–720 (1997)Google Scholar
  2. 2.
    Bichsel, M.: Strategies of Robust Object Recognition for the Identification of Human Faces. ETH, Zurich (1991)Google Scholar
  3. 3.
    Brunelli, R., Pggio, T.: Face recognition: Features versus templates. PAMI 15(10), 1042–1062 (1993)Google Scholar
  4. 4.
    Bundschuh, B.: A linear predictor as a regularization function in adaptive image restoration and reconstruction. In: Chetverikov, D., Kropatsch, W.G. (eds.) CAIP 1993. LNCS, vol. 719. Springer, Heidelberg (1993)Google Scholar
  5. 5.
    Bundschuh, H., Schulz, B., Schneider, D.: Adaptive least squares image restoration using whitening filters of short length. In: Second HST Image Restoration Workshop (1993)Google Scholar
  6. 6.
    Chen, H.F., Belhumeur, P.N., Jacobs, D.W.: In search of illumination invariants. In: CVPR 2000, pp. I:254–261 (2000)Google Scholar
  7. 7.
    Cox, M.L., Miller, I.J., Bloom, J.A.: Digital Watermarking. Morgan Kaufmann, San Francisco (2002)Google Scholar
  8. 8.
    Depovere, T., Kalker, G., Linnartz, J.P.: Improved watermark detection using filtering before correlation. In: IEEE Int. Conf. on Image Processing, pp. I:430– 434 (1998)Google Scholar
  9. 9.
    Faugeras, O.D., Pratt, W.K.: Decorrelation methods of texture feature extraction. PAMI 2(4), 323–332 (1980)Google Scholar
  10. 10.
    Haralick, R.M., Shapiro, L.G.: Computer and robot vision. Addison-Wesley, Reading (1992)Google Scholar
  11. 11.
    Jacobs, D.W., Belhumeur, P.N., Basri, R.: Comparing images under variable illumination. In: CVPR 1998, pp. 610–617 (1998)Google Scholar
  12. 12.
    Jain, A.K.: Fundamentals of digital image processing. Prentice Hall, Englewood Cliffs (1989)zbMATHGoogle Scholar
  13. 13.
    Lades, M., Vorbruggen, J.C., Buhmann, J., Lange, J., von der Malsburg, C., Wurtz, R.P., Konen, W.: Distortion invariant object recognition in the dynamic link architecture. TC 42(3), 300–311 (1993)Google Scholar
  14. 14.
    Lin, Z., Attikiouzel, Y.: Two-dimensional linear prediction model-based decorrelation method. PAMI 11(6), 661–665 (1989)Google Scholar
  15. 15.
    Osadchy, M., Lindenbaum, M., Jacobs, D.W.: Whitening for photometric comparison of smooth surfaces under varying illumination. In: IEEE workshop on Statistical and Computational Theories of Vision (October 2003)Google Scholar
  16. 16.
    Papoulis, A.: Probability, Random Variables, and Stochastic Processes, 3rd edn. McGraw Hill, New York (1991)Google Scholar
  17. 17.
    Pratt, W.K.: Digital Image Processing, 1st edn. Wiley, Chichester (1978)Google Scholar
  18. 18.
    Ravela, S., Luo, C.: Appearance-based global similarity retrieval of images. In: Bruce Croft, W. (ed.). Kluwer Academic Publisher, Dordrecht (2000)Google Scholar
  19. 19.
    Van Trees, H.L.: Detection, Estimation, and Modulation Theory Part I. Wiley, New-York (1965)Google Scholar
  20. 20.
    Yaroslavsky, L.P.: Digital Picture Processing. An Introduction. Springer, Heidelberg (1985)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Margarita Osadchy
    • 1
  • Michael Lindenbaum
    • 2
  • David Jacobs
    • 3
  1. 1.NEC Laboratories AmericaPrincetonUSA
  2. 2.Dept. of Computer ScienceThe TechnionHaifaIsrael
  3. 3.Dept. of Computer ScienceThe University of MarylandCollege Park

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