Parallel multiscale stereo matching using adaptive smoothing

  • Jer-Sen ChenEmail author
  • Gérard Medioni
Stereo And Reconstruction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 427)


We have shown a multiscale coarse-to-fine hierarchical matching of stereo pairs which uses adaptive smoothing to extract the matching primitives. The number of matching primitives at coarse scale is small, therefore reducing the number of potential matches, which in return increases the reliability of the matching results. A dense disparity can be obtained at a fine scale where the density of edgels is very high. The control strategy is very simple compared to other multiscale approaches such as the ones using Gaussian scale space, this results from the accuracy of edges detected by adaptive smoothing at different scales. The simplicity of the control strategy is especially important for low-level processing, and makes parallel implementation quite simple.


Coarse Scale Uniqueness Constraint Stereo Match Stereo Pair Intermediate Scale 
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.


  1. [Asada86]
    H. Asada and M. Brady. “The Curvature Primal Sketch”, IEEE Tran. on Pattern Analysis and Machine Intelligence, Vol. 8, No. 1, January 1986, pp 2–14.Google Scholar
  2. [Barnard82]
    Barnard, S. and Fischler, M., “Computational Stereo”, ACM Computing Surveys, Vol. 14, No. 4, December 1982, pp 553–572.Google Scholar
  3. [Burt83]
    Burt, P.J., “Fast Algorithm for Estimating Local Image Properties,” Journal of Computer Vision, Graphics, Image Processing, Vol. 21, March 1983, pp. 368–382.Google Scholar
  4. [Chen89]
    Chen, J.S., “Accurate Edge Detection for Multiple Scale Processing”, Ph.D. thesis, University of Southern California, October 1989.Google Scholar
  5. [Drumheller86]
    Drumheller, M., Poggio, T., “On Parallel Stereo”, Proceedings of the 1986 IEEE International Conference on Robotics and Automation, April 1986, pp 1439–1488.Google Scholar
  6. [Grimson81]
    Grimson, W.E.L., From Images to Surfaces, MIT Press, Cambridge, USA, 1981.Google Scholar
  7. [Hillis85]
    D. Hillis. The Connection Machine. MIT Press, Cambridge, MA, 1985.Google Scholar
  8. [Marr82]
    Marr, D., Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman and Co., San Francisco, 1982.Google Scholar
  9. [Perona87]
    Perona, P., Malik, J., “Scale Space and Edge Detection using Anisotropic Diffusion”, Proceedings of the IEEE Workshop on Computer Vision, Miami Beach, Fl. 1987, pp 16–22.Google Scholar
  10. [Saintmarc89]
    Saint-Marc, P., Chen J.S. and Medioni, G., “Adaptive Smoothing: A General Tool for Early Vision”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Diego, June 1989.Google Scholar
  11. [Witkin83]
    Witkin, A.P., “Scale Space Filtering”, Proceedings of International Joint Conference on Artificial Intelligence, pp 1019–1022, Karlsruhe, 1983.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1990

Authors and Affiliations

  1. 1.Institute for Robotics and Intelligent SystemsUniversity of Southern CaliforniaLos Angeles

Personalised recommendations