Experiments with a new area-based stereo algorithm

  • A. Fusiello
  • V. Roberto
  • E. Trucco
Session 7: Motion & Stereo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)


We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm uses multiple windows and left-right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both robustness and efficiency.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    P. Anandan. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2:283–310, 1989.Google Scholar
  2. 2.
    I. J. Cox, S. Hingorani, B. M. Maggs, and S. B. Rao. A maximum likelihood stereo algorithm. Computer Vision and Image Understanding, 63(3):542–567, May 1996.Google Scholar
  3. 3.
    O. Faugeras, B. Hotz, H. Mathieu, T. Viéville, Z. Zhang, P. Fua, E. Théron, L. Moll, G. Berry, J. Vuillemin, P. Bertin, and C. Proy. Real-time correlation-based stereo: algorithm, implementation and applications. Technical Report 2013, Unité de recherche INRIA Sophia-Antipolis, Août 1993.Google Scholar
  4. 4.
    P. Fua. Combining stereo and monocular information to compute dense depth maps that preserve depth discontinuities. In Proceedings of the International Joint Conference on Artificial Intelligence, Sydney, Australia, August 1991.Google Scholar
  5. 5.
    A Fusiello, E. Trucco, and A. Verri. Rectification with unconstrained stereo geometry. Submitted to ICCV'98.Google Scholar
  6. 6.
    W.E.L. Grimson. Computational experiments with a feature based stereo algorithm. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(1):1734, 1985.Google Scholar
  7. 7.
    S. S. Intille and A. F. Bobick. Disparity-space images and large occlusion stereo. In Jan-Olof Eklundh, editor, European Conference on Computer Vision, pages 179–186, Stockholm, Sweden, May 1994. Springer-Verlag.Google Scholar
  8. 8.
    M. R. M. Jenkin, A. D. Jepson, and J. K. Tsotsos. Techniques for disparity measurements. CVGIP: Image Understanding, 53(1):14–30, 1991.Google Scholar
  9. 9.
    T. Kanade and M. Okutomi. A stereo matching algorithm with an adaptive window: Theory and experiments. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(9):920–932, September 1994.Google Scholar
  10. 10.
    J. J. Little and W. E. Gillett. Direct evidence for occlusions in stereo and motion. Image and Vision Computing, 8(4):328–340, 1990.Google Scholar
  11. 11.
    L. Matthies, T. Kanade, and R. Szelisky. Kalman filter based algorithms for estimating depth from image sequences. International Journal of Computer Vision, 3:209–236, 1989.Google Scholar
  12. 12.
    Y. Ohta and T. Kanade. Stereo by intra-and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence, 7(2):139–154, 1985.Google Scholar
  13. 13.
    E. Trucco, V. Roberto, S. Tinonin, and M. Corbatto. SSD disparity estimation for dynamic stereo. In R. B. Fisher and E. Trucco, editors, Proceedings of the British Machine Vision Conference, pages 342–352. BMVA Press, 1996.Google Scholar
  14. 14.
    Y. Yang and A. L. Yuille. Multilevel enhancement and detection of stereo disparity surfaces. Artificial Intelligence, 78(1–2):121–145, October 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • A. Fusiello
    • 1
  • V. Roberto
    • 1
  • E. Trucco
    • 2
  1. 1.Machine Vision Laboratory, Dept. of InformaticsUniversity of UdineItaly
  2. 2.Dept. of Computing and Electrical EngineeringHeriot-Watt UniversityUK

Personalised recommendations