Disparity Using Feature Points in Multi Scale

  • Ilkay Ulusoy
  • Edwin R. Hancock
  • Ugur Halici
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2396)

Abstract

In this paper we describe a statistical framework for binocular disparity estimation. We use a bank of Gabor filters to compute multiscale phase signatures at detected feature points. Using a von Mises distribution, we calculate correspondence probabilities for the feature points in different images using the phase differences at different scales. The disparity map is computed using the set of maximum likelihood correspondences.

Keywords

Feature Point Population Vector Binocular Disparity Multi Scale Stereo Correspondence 
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.
    Anzai, A., Ohzawa, I., Freeman, R. D.: Neural mechanisms for encoding binocular disparity: Receptive field position vs. phase. Journal of Neurophysiology, vol. 82, no. 2, pp. 874–890, 1999.Google Scholar
  2. 2.
    Anzai, A., Ohzawa, I., Freeman, R. D.: Neural mechanisms for processing binocular information I. Simple cells. Journal of Neurophysiology, vol. 82, no. 2, pp. 891–908, 1999.Google Scholar
  3. 3.
    Anzai, A., Ohzawa, I., Freeman, R. D.: Neural mechanisms for processing binocular information II. Complex cells. Journal of Neurophysiology, vol. 82, no. 2, pp. 909–924, 1999.Google Scholar
  4. 4.
    DeAngelis, G.: Seeing in three dimension: the neurophysiology of stereopsis. Trends in Cognitive Science, vol. 4, no. 3, pp. 80–89, 2000.CrossRefMathSciNetGoogle Scholar
  5. 5.
    Fleet, D. J., Wagner, H., Heeger, D. J.: Neural encoding of binocular disparity: Energy models, position shifts and phase shifts. Vision Research, vol. 36, no. 12, pp. 1839–1857, 1996.CrossRefGoogle Scholar
  6. 6.
    Jenkin, M. R. M., Jepson, A. D.: Recovering local surface structure through local phase difference measurements. CVGIP: Image Understanding, vol. 59, no. 1, pp. 72–93, 1994.CrossRefGoogle Scholar
  7. 7.
    Jepson, A. D., Fleet, D. J.: Scale space singularities. Lecture Notes in Computer Science, vol. 427, pp. 50–55, 1990.Google Scholar
  8. 8.
    Jepson, A. D., Fleet, D. J.: Phase singularities in scale space. Image and Vision Computing, vol. 9, no. 5, pp. 338–343, 1991.CrossRefGoogle Scholar
  9. 9.
    Ludtke, N., Wilson, R. C., Hancock, E. R.: Tangent fileds from population coding. Lecture Notes in Computer Science, vol. 1811, pp. 584–593, 2000.Google Scholar
  10. 10.
    Marr, D., Poggio, T.: A computational theory of human stereo vision. Proceedings of the Royal Society of London, B207, pp. 187–217, 1979.Google Scholar
  11. 11.
    Pollard, S. B., Mayhew, J. E. W., Frisby, J. P.: PMF: A stereo correspondence algorithm using a disparity gradient limit. Perception, vol. 14, pp. 449–470, 1985.CrossRefGoogle Scholar
  12. 12.
    Qian, N.: Computing stereo disparity and motion with known binocular cell properties. Neural Computation, vol. 6, no. 3, pp. 390–404, 1994.CrossRefGoogle Scholar
  13. 13.
    Qian, N., Zhu, Y.: Physiological computation of binocular disparity. Vision Research, vol. 37, no. 13, pp. 1811–1827, 1997.CrossRefGoogle Scholar
  14. 14.
    Qian, N.: Relationship between phase and Energy methods for disparity computation. Neural Computation, 12, pp. 279–292, 2000.CrossRefGoogle Scholar
  15. 15.
    Sanger, T. D.: Stereo disparity computation using Gabor filters. Biol. Cybern., 59, pp. 405–418, 1988.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ilkay Ulusoy
    • 1
  • Edwin R. Hancock
    • 2
  • Ugur Halici
    • 1
  1. 1.Computer Vision and Artificial Neural Networks Lab.Middle East Technical UniversityAnkaraTurkey
  2. 2.Department of Computer ScienceUniversity of YorkUK

Personalised recommendations