Skip to main content
Springer Nature Link
Log in

Dense Motion and Disparity Estimation Via Loopy Belief Propagation

  • Conference paper
Computer Vision – ACCV 2006 (ACCV 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3852))

Included in the following conference series:

  • 2271 Accesses

Abstract

We describe a method for computing a dense estimate of motion and disparity, given a stereo video sequence containing moving non-rigid objects. In contrast to previous approaches, motion and disparity are estimated simultaneously from a single coherent probabilistic model that correctly accounts for all occlusions, depth discontinuities, and motion discontinuities. The results demonstrate that simultaneous estimation of motion and disparity is superior to estimating either in isolation, and show the promise of the technique for accurate, probabilistically justified, scene analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Waxman, A., Duncan, J.: Binocular image flows: Steps towards stereo-motion fusion. IEEE Trans. on PAMI 8, 715–729 (1986)

    Google Scholar 

  2. Williams, O., Isard, M., MacCormick, J.: Estimating disparity and occlusions in stereo video sequences. In: Proc. CVPR (2005)

    Google Scholar 

  3. Chang, Y., Aggarwal, J.: Line correspondences from cooperating spatial and temporal grouping processes for a sequence of images. Computer Vision and Image Understanding 67, 186–201 (1997)

    Article  Google Scholar 

  4. Ho, A., Pong, T.: Cooperative fusion of stereo and motion. Pattern Recognition 29, 121–130 (1996)

    Article  Google Scholar 

  5. Wang, W., Duncan, J.: Recovering the three dimensional motion and structure of multiple moving objects from binocular image flows. Computer Vision and Image Understanding 63, 430–446 (1996)

    Article  Google Scholar 

  6. Sun, J., Shum, H.Y., Zheng, N.N.: Stereo matching using belief propagation. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 510–524. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  7. Kolmogorov, V., Zabih, R.: Computing visual correspondence with occlusions using graph cuts. In: Proc. Int. Conf. on Computer Vision (2001)

    Google Scholar 

  8. Criminisi, A., Shotton, J., Blake, A., Torr, P.: Gaze manipulation for one-to-one teleconferencing. In: Proc. Int. Conf. on Computer Vision (2003)

    Google Scholar 

  9. Leung, C., Appleton, B., Lovell, B.C., Sun, C.: An energy minimisation approach to stereo-temporal dense reconstruction. In: Proc. Int. Conf. on Pattern Recognition (2004)

    Google Scholar 

  10. Zhang, L., Curless, B., Seitz, S.M.: Spacetime stereo: Shape recovery for dynamic scenes. In: Proc. CVPR., vol. 2, pp. 367–374 (2003)

    Google Scholar 

  11. Shao, J.: Generation of temporally consistent multiple virtual camera views from stereoscopic image sequences. Int. J. Comput. Vision 47, 171–180 (2002)

    Article  MATH  Google Scholar 

  12. Vedula, S., Baker, S., Rander, P., Collins, R., Kanade, T.: Three-dimensional scene flow. In: Proc. Int. Conf. on Computer Vision, vol. 2, pp. 722–729 (1999)

    Google Scholar 

  13. Belhumeur, P.: A Bayesian approach to binocular stereopsis. Int. J. Computer Vision 19, 237–260 (1996)

    Article  Google Scholar 

  14. Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Computer Vision (2002)

    Google Scholar 

  15. Baker, S., Matthews, I.: Lucas-Kanade 20 years on: A unifying framework. Int. J. Comput. Vision 56, 221–255 (2004)

    Article  Google Scholar 

  16. Isard, M., MacCormick, J.: Dense motion and disparity estimation via loopy belief propagation. Technical report, Microsoft Research (2005)

    Google Scholar 

  17. Blake, A., et al.: Bi-layer segmentation of binocular stereo video. In: Proc. CVPR (2005)

    Google Scholar 

  18. Yedidia, J., Freeman, W., Weiss, Y.: Understanding Belief Propagation and its Generalizations. In: Exploring Artificial Intelligence in the New Millennium, Elsevier Science, Amsterdam (2003)

    Google Scholar 

  19. Felzenszwalb, P., Huttenlocher, D.: Efficient belief propagation for early vision. In: Proc. CVPR (2004)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Microsoft Research Silicon Valley, Mountain View, California, USA

    Michael Isard & John MacCormick

Authors
  1. Michael Isard
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. John MacCormick
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Visual Information Technology, International Institute of Information Technology, Hyderabad, India

    P. J. Narayanan

  2. Department of Computer Science, Columbia University, 500 West 120th Street, NY 10027, New York, USA

    Shree K. Nayar

  3. Microsoft Research Asia, P.O. Box, Beijing, P.R. China

    Heung-Yeung Shum

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Isard, M., MacCormick, J. (2006). Dense Motion and Disparity Estimation Via Loopy Belief Propagation. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3852. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612704_4

Download citation

  • DOI: https://doi.org/10.1007/11612704_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31244-4

  • Online ISBN: 978-3-540-32432-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation