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A High-Speed Parallel Architecture for Stereo Matching

  • Sungchan Park
  • Hong Jeong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)

Abstract

The stereo matching algorithm based on the belief propagation (BP) has the low matching error as the global method, but has the disadvantage of a long processing time. In addition to a low error of less than 2.6% in the Middlebury image simulation, a new architecture based on BP shows a high-speed parallel VLSI structure of the time complexity O(N), at properly small iterations, so that it can be useful as a chip in the real-time application like robots and navigations.

Keywords

Processing Element Belief Propagation Stereo Match VLSI Architecture Stereo Match Algorithm 
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.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1-3), 7–42 (2002)MATHCrossRefGoogle Scholar
  2. 2.
    Kimura, S., Shinbo, T., Yamaguchi, H., Kawamura, E., Naka, K.: A Convolver-Based Real-Time Stereo Machine (SAZAN). In: Proc. Computer Vision and Pattern Recognition, vol. 1, pp. 457–463 (1999)Google Scholar
  3. 3.
    Hirschmuller, H.: Improvements in real-time correlation-based stereo vision. In: IEEE Workshop on Stereo and Multi-Baseline Vision, pp. 141–148 (December 2001)Google Scholar
  4. 4.
    Hariyama, M., et al.: Architecture of a stereo matching VLSI processor based on hierarchically parallel memory access. In: The 2004 47th Midwest Symposium on Circuits and Systems, vol. 2, pp. 245–247 (2004)Google Scholar
  5. 5.
    Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: ICCV, vol. 2, pp. 508–515 (2001)Google Scholar
  6. 6.
    Felzenszwalb, P.F., Huttenlocher, D.R.: Efficient belief propagation for early vision. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. I261–I268 (2004)Google Scholar
  7. 7.
    Jordan, M.I.: An Introduction to Probabilistic Graphical Models (in preparation) Google Scholar
  8. 8.
    Wainwright, M.J., Jaakkola, T., Willsky, A.S.: Tree-based reparameterization framework for analysis of sum-product and related algorithms. IEEE Transactions on Information Theory 49(5), 1120–1146 (2003)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sungchan Park
    • 1
  • Hong Jeong
    • 1
  1. 1.Electronic amd Electrical EngineeringPohang University of Science and TechnologyPohang, KyungbukSouth Korea

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