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A Linear Image-Pair Model and the Associated Hypothesis Test for Matching

  • Gregory Cox
  • Gerhard de Jager
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2383)

Abstract

A statistical model is developed for the image pair and used to derive a minimum-error hypothesis test for matching. For reasons of tractability a multivariate normal image model and linear dependence between images are assumed. As one would expect, the optimal test outperforms the standard approaches when the assumed model is in force, but the extent of the optimal test’s superiority suggests that there is significant potential for improvement on the standard methods of assessing image similarity.

Keywords

Image Pair Optimal Test Likelihood Ratio Test Statistic Minimum Error Rate Synthesise Image Pair 
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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References

  1. 1.
    M. Boninsegna and M. Rossi, “Similarity measures in computer vision,” Pattern Recognition Letters, vol. 15, pp. 1255–1260, 1994.CrossRefGoogle Scholar
  2. 2.
    R. Brunelli and S. Messelodi, “Robust estimation of correlation with applications to computer vision,” Pattern Recognition, vol. 28, pp. 833–841, June 1995.Google Scholar
  3. 3.
    A. Venot, J. F. Lebruchec, and J. C. Roucayrol, “A new class of similarity measures for robust image registration,” Computer Vision, Graphics, and Image Processing, vol. 28, pp. 176–184, 1984.CrossRefGoogle Scholar
  4. 4.
    D. N. Bhat and S. K. Nayar, “Ordinal measures for image correspondence,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 415–423, Apr. 1998.Google Scholar
  5. 5.
    P. Viola and W. M. Wells III, “Alignment by maximization of mutual information,” in International Conference on Computer Vision, pp. 16–23, June 1995.Google Scholar
  6. 6.
    T. Buzug and J. Weese, “Similarity measures for subtraction methods in medical imaging,” in 18th Annual International Conference of the IEEE EMBS, p. 140, 1996.Google Scholar
  7. 7.
    G. P. Penny, J. Weese, J. A. Little, P. Desmedt, D. L. G. Hill, and D. J. Hawkes, “A comparison of similarity measures for use in 2D-3D medical image registration,” in First Conference on Medical Image Computing and Computer Assisted Intervention, vol. 1496, (Cambridge, MA, USA), pp. 1153–1161, 1998.Google Scholar
  8. 8.
    E. H. W. Meijering, W. J. Niessen, and M. A. Vergiever, “Retrospective motion correction in digital subtraction angiography: A review,” IEEE Transactions on Medical Imaging, vol. 18, pp. 2–21, Jan. 1999.Google Scholar
  9. 9.
    P. Aschwanden and W. Guggenbül, “Experimental results from a comparative study on correlation-type registration algorithms,” in International Workshop on Robust Computer Vision, no. 2, pp. 268–289, Mar. 1992.Google Scholar
  10. 10.
    B. R. Hunt, “Nonstationary statistical image models (and their application to image data compression),” Computer Graphics and Image Processing, vol. 12, pp. 173–186, 1980.CrossRefGoogle Scholar
  11. 11.
    P. B. Chapple and D. C. Bertilone, “Stochastic simulation of infrared non-Gaussian terrain imagery,” Optics Communications, no. 150, pp. 71–76, 1998.Google Scholar
  12. 12.
    G. E. Johnson, “Constructions of particular random processes,” Proceedings of the IEEE, vol. 82, pp. 270–285, Feb. 1994.Google Scholar
  13. 13.
    D. Kazakos and P. Papantoni-Kazakos, Detection and Estimation. Computer Science Press, 1990.Google Scholar
  14. 14.
    G. S. Cox, ”Designing Hypothesis Tests for Digital Image Matching”. PhD thesis, University of Cape Town, December 2000.Google Scholar
  15. 15.
    P. J. Huber, Robust Statistics. John Wiley and Sons, 1981.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Gregory Cox
    • 1
  • Gerhard de Jager
    • 2
  1. 1.Machine Intelligence Group, DebTech ResearchDe Beers Consolidated MinesJohannesburgSouth Africa
  2. 2.Digital Image Processing Laboratory, Department of Electrical EngineeringUniversity of Cape TownRondeboschSouth Africa

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