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Extraction of Layers of Similar Motion Through Combinatorial Techniques

  • Romain Dupont
  • Nikos Paragios
  • Renaud Keriven
  • Phillipe Fuchs
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)

Abstract

In this paper we present a new technique to extract layers in a video sequence. To this end, we assume that the observed scene is composed of several transparent layers, that their motion in the 2D plane can be approximated with an affine model. The objective of our approach is the estimation of these motion models as well as the estimation of their support in the image domain. Our technique is based on an iterative process that integrates robust motion estimation, MRF-based formulation, combinatorial optimization and the use of visual as well as motion features to recover the parameters of the motion models as well as their support layers. Special handling of occlusions as well as adaptive techniques to detect new objects in the scene are also considered. Promising results demonstrate the potentials of our approach.

Keywords

Residual Error Motion Estimation Motion Model Image Domain Minimum Description Length 
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.
    Wang, J., Adelson, E.: Representing Moving Images with Layers. IEEE Transactions on Image Processing 3, 625–638 (1994)CrossRefGoogle Scholar
  2. 2.
    Bergen, J.R., Anandan, P., Hanna, K.J., Hingorani, R.: Hierarchical model-based motion estimation. In: ECCV 1992: Proceedings of the Second European Conference on Computer Vision, pp. 237–252. Springer, Heidelberg (1992)Google Scholar
  3. 3.
    Ayer, S., Sawhney, H.: Layered Representation of Motion Video Using Robust Maximum-Likelihood Estimation of Mixture Models and MDL Encoding. In: IEEE International Conference in Computer Vision, Caibridge, USA, pp. 777–784 (1995)Google Scholar
  4. 4.
    Weiss, Y.: Smoothness in layers: Motion segmentation using nonparametric mixture estimation. In: CVPR 1997: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR 1997), Washington, DC, USA, p. 520. IEEE Computer Society, Los Alamitos (1997)Google Scholar
  5. 5.
    Shanon, X.J., Black, M.J., Jepson, A.D.: Skin and bones: Multi-layer, locally affine, optical flow and regularization with transparency. In: CVPR 1996: Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR 1996), Washington, DC, USA, p. 307. IEEE Computer Society, Los Alamitos (1996)Google Scholar
  6. 6.
    Cremers, D., Soatto, S.: Variational space-time motion segmentation. In: International Conference on Computer Vision (ICCV), pp. 886–893 (2003)Google Scholar
  7. 7.
    Horn, B., Schunck, B.: Determinating Optical Flow. Artificial Intelligence 17, 185–203 (1981)CrossRefGoogle Scholar
  8. 8.
    Barron, J., Fleet, D., Beauchemin, S., Burkitt, T.: Performance of optical flow techniques. In: Computer Vision and Pattern Recognition (CVPR), pp. 236–242 (1992)Google Scholar
  9. 9.
    Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: 8th European Conference on Computer Vision (ECCV), pp. 25–36 (2004)Google Scholar
  10. 10.
    Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Underst. 63, 75–104 (1996)CrossRefGoogle Scholar
  11. 11.
    Tikhonov, A.: Ill-Posed Problems in Natural Sciences. Coronet (1992)Google Scholar
  12. 12.
    Odobez, J.M., Bouthemy, P.: Robust multiresolution estimation of parametric motion models. Journal of Visual Communication and Image Representation 6, 348–365 (1995)CrossRefGoogle Scholar
  13. 13.
    Black, M.J., Jepson, A.D.: Estimating optical flow in segmented images using variable-order parametric models with local deformations. IEEE Trans. Pattern Anal. Mach. Intell. 18, 972–986 (1996)CrossRefGoogle Scholar
  14. 14.
    Darrell, T., Pentland, A.P.: Cooperative robust estimation using layers of support. IEEE Trans. Pattern Anal. Mach. Intell. 17, 474–487 (1995)CrossRefGoogle Scholar
  15. 15.
    Xiao, J., Shah, M.: Motion layer extraction in the presence of occlusion using graph cut. In: CVPR, vol. (2), pp. 972–979 (2004)Google Scholar
  16. 16.
    Zabih, R., Kolmogorov, V.: Spatially coherent clustering using graph cuts. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 437–444 (2004)Google Scholar
  17. 17.
    Irani, M., Anandan, P.: A unified approach to moving object detection in 2d and 3d scenes. IEEE Trans. Pattern Anal. Mach. Intell. 20, 577–589 (1998)CrossRefGoogle Scholar
  18. 18.
    Duda, R., Hart, P.: Pattern Classification and Scene Analysis. John Wiley & Sons, Chichester (1973)zbMATHGoogle Scholar
  19. 19.
    Boykov, Y., Jolly, M.P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV, pp. 105–112 (2001)Google Scholar
  20. 20.
    Besag, J.: On the statistical analysis of dirty images. Journal of Royal Statistics Society 48, 259–302 (1986)zbMATHMathSciNetGoogle Scholar
  21. 21.
    Chou, P., Brown, C.: The theory and practice of bayesian image labeling. International Journal of Computer Vision 4, 185–210 (1990)CrossRefGoogle Scholar
  22. 22.
    Geman, S., Geman, D.: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 721–741 (1984)zbMATHCrossRefGoogle Scholar
  23. 23.
    Boykov, Y., Veksler, O., Zabih, R.: Fast Approximate Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 1222–1239 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Romain Dupont
    • 1
    • 2
  • Nikos Paragios
    • 1
  • Renaud Keriven
    • 1
  • Phillipe Fuchs
    • 2
  1. 1.Atlantis Research GroupCERTIS ENPCMarne-La-ValleeFrance
  2. 2.Centre de RobotiqueCAOR ENSMPParisFrance

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