Local stereoscopic depth estimation using ocular stripe maps

  • Kai-Oliver Ludwig
  • Heiko Neumann
  • Bernd Neumann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 588)


Visual information is represented in the primate visual cortex (area 17, layer 4B) in a peculiar structure of alternating bands of left and right eye dominance. Recently, a number of computational algorithms based on this ocular stripe map architecture have been proposed, from which we selected the cepstral filtering method of Y. Yeshurun & E.L. Schwartz [11] for fast disparity computation due to its simplicity and robustness. The algorithm has been implemented and analyzed. Some special deficiencies have been identified. The robustness against noise and image degradations such as rotation and scaling has been evaluated. We made several improvements to the algorithm. For real image data the cepstral filter behaves like a square autocorrelation of a bandpass filtered version of the original image. The discussed framework is now a reliable single-step method for local depth estimation.


stereopsis primary visual cortex ocular stripe maps cepstrum local depth estimation 


  1. 1.
    S.T. Barnard and M.A. Fischler: Computational and Biological Models of Stereo Vision. In Proc. IU Workshop, Pittsburgh, PA, USA, September 11–13 (1990) 439–448Google Scholar
  2. 2.
    B.P. Bogert, M. J.R. Healy, and J.W. Tukey. The quefrency alanysis of time series for echoes: cepstrum, cross-cepstrum, and saphe cracking. In Proceedings: Symposium on Time Series Analysis (1963) 209–243Google Scholar
  3. 3.
    U.R. Dhond and J.K. Aggarwal. Structure from Stereo — A Review. IEEE Trans. on Systems, Man, and Cybernetics, 19(6) (1989) 1489–1510Google Scholar
  4. 4.
    B.M. Dow. Nested maps in macaque monkey visual cortex. In K.N. Leibovic, editor, The Science of Vision, Springer, New York (1990) 84–124Google Scholar
  5. 5.
    K.-O. Ludwig. Untersuchung der Cepstrumtechnik zur Querdisparitätsbestimmung für die Tiefenschätzung bei fixierenden Stereokonfigurationen. Technical Report, Fachbereich Informatik, Universit” at Hamburg (1991)Google Scholar
  6. 6.
    K.-O. Ludwig, B. Neumann, and H. Neumann. Robust Estimation of Local Stereoscopic Depth. In International Workshop on Robust Computer Vision (IWRCV '92), Bonn, Germany, October 9–12 (1992)Google Scholar
  7. 7.
    H.A. Mallot, W. von Seelen, and F. Giannakopoulos. Neural Mapping and Space-Variant Image Processing. Neural Networks, 3 (1990) 245–263Google Scholar
  8. 8.
    D. Marr. Vision. W.H. Freeman and Company, San Francisco (1982)Google Scholar
  9. 9.
    T.J. Olson and D.J. Coombs. Real-Time Vergence Control for Binocular Robots. Technical Report 348, Department of Computer Science, University of Rochester (1990)Google Scholar
  10. 10.
    T. Poggio, V. Torre, and C. Koch. Computational vision and regularization theory. Nature, 317 (1985) 315–319Google Scholar
  11. 11.
    Y. Yeshurun and E.L. Schwartz. Neural Maps as Data Structures: Fast Segmentation of Binocular Images. In E.L. Schwartz, editor, Computational Neuroscience, Chap. 20, The MIT Press (1990) 256–266Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Kai-Oliver Ludwig
    • 1
  • Heiko Neumann
    • 1
  • Bernd Neumann
    • 1
  1. 1.FB InformatikUniversität HamburgHamburg 50Germany

Personalised recommendations