Joint estimation-segmentation of optic flow

  • Étienne Mémin
  • Patrick Pérez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1407)


In this paper we address the intricate issue of jointly recovering the apparent velocity field between two consecutive frames and its underlying partition. We design a global cost functional including robust estimators. These estimators enable to deal with the large deviations occurring in the different energy terms and offer the possibility to introduce a simple coupling between a dense optical flow field and a segmentation. This coupling is also reinforced by a parametric likeness term. The resulting estimation-segmentation model thus involves a tight cooperation between a local estimation process and a global modelization. The minimization of the final cost function is conducted efficiently by a multigrid optimization algorithm.


Optical Flow Motion Estimation Markov Random Field Motion Field Global Deformation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    E.H. Adelson and J.Y.A. Wang. Representing moving images with layers. IEEE Trans. Pattern Anal., Machine Intell., 5(3):625–638, 1994.Google Scholar
  2. 2.
    G. Adiv. Determining three-dimensional motion and structure from optical flow generated by several moving objects. IEEE Trans. Pattern Anal. Machine Intell., 7:384–401, Jul 1985.Google Scholar
  3. 3.
    S. Ayer and H.S. Sawhney. Layered representation of motion video using robust maximum-likelihood estimation of mixture models and Mdl encoding. In Proc. Int. Conf. Computer Vision, pages 777–784, June 1995.Google Scholar
  4. 4.
    J. Barron, D. Fleet, and S. Beauchemin. Performance of optical flow techniques. Int. J. Computer Vision, 12(1):43–77, 1994.CrossRefGoogle Scholar
  5. 5.
    M. Black and P. Anandan. The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 63(1):75–104, 1996.CrossRefGoogle Scholar
  6. 6.
    M. Black and P. Jepson. Estimating optical flow in segmented images using variable-order parametric models with local deformations. IEEE Trans. Pattern Anal. Machine Intell., 18(10):972–986, 1996.CrossRefGoogle Scholar
  7. 7.
    P. Bouthemy and E. Francois. Motion segmentation and qualitative dynamic scene analysis from an image sequence. Int. J. Computer Vision, 10(2):157–182, 1993.CrossRefGoogle Scholar
  8. 8.
    M. M. Chang, A. M. Tekalp, and M. I. Sezan. Simultaneous motion estimation and segmentation. IEEE Trans. Image Processing, 6(9):1326–1333, 1997.CrossRefGoogle Scholar
  9. 9.
    P. Charbonnier, L. Blanc-Féraud, G. Aubert, and M. Barlaud. Deterministic edgepreserving regularization in computed imaging. IEEE Trans. Image Processing, 6(2):298–311, 1997.CrossRefGoogle Scholar
  10. 10.
    T. Darrell and A.P. Pentland. Cooperative robust estimation using layers of support. IEEE Trans. Pattern Anal. Machine Intell., 17(5):474–487, 1995.CrossRefGoogle Scholar
  11. 11.
    W. Enkelmann. Investigation of multigrid algorithms for the estimation of optical flow fields in image sequences. Comp. Vision Graph. and Image Proces., 43:150–177, 1988.CrossRefGoogle Scholar
  12. 12.
    D. Geman and G. Reynolds. Constrained restoration and the recovery of discontinuities. IEEE Trans. Pattern Anal. Machine Intell., 14(3):367–383, 1992.CrossRefGoogle Scholar
  13. 13.
    B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 17:185–203, 1981.CrossRefGoogle Scholar
  14. 14.
    Y. Leclerc. Constructing simple stable descriptions for image partitioning. Int. J. Computer Vision, 3:73–102, 1989.CrossRefGoogle Scholar
  15. 15.
    E. Mémin and P. Pérez. Dense estimation and object-based segmentation of the optical flow with robust techniques. IEEE Trans. Image Processing, 5(1), 1998.Google Scholar
  16. 16.
    E. Mémin and P. Pérez. A multigrid approach for hierarchical motion estimation. In Proc. Int. Conf. Computer Vision, pages 933–938, 1998.Google Scholar
  17. 17.
    D.W. Murray and H. Buxton. Scene segmentation from visual motion using global optimization. IEEE Trans. Pattern Anal. Machine Intell., 9(2):220–228, Mar 1987.CrossRefGoogle Scholar
  18. 18.
    C. Stiller. Object-based estimation of dense motion fields. IEEE Trans. Image Processing, 6(2):234–250, 1997.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Étienne Mémin
    • 1
    • 2
  • Patrick Pérez
    • 3
  1. 1.Valoria, Université de Bretagne SudVannesFrance
  2. 2.IRISARennes Cedex
  3. 3.IRISA/INRIARennes CedexFrance

Personalised recommendations