A stochastic dynamical system for optical flow estimation

2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops 2009

Volumn , Issue , 2009, Pages 711-718

  Willert, Volker ^a   Eggert, Julian ^b  

^a DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

^b HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; BELIEF PROPAGATION; DYNAMIC FEATURES; FLOW DYNAMICS; GAUSSIANS; IMAGE SEQUENCE; INPUT STREAMS; MIXTURE MODEL; OPTICAL FLOW ESTIMATION; SMOOTHNESS CONSTRAINTS; SPATIAL SMOOTHNESS; STOCHASTIC DYNAMICAL SYSTEM; TRANSITION PROBABILITIES; VISUAL DATA; VISUAL MOTION;

COMPUTER VISION; DATA FLOW ANALYSIS; DYNAMICAL SYSTEMS;

OPTICAL FLOWS;

EID: 77953209328     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCVW.2009.5457632     Document Type: Conference Paper

References (14)

1. S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski. A database and evaluation methodology for optical flow. In ICCV, 2007.
2. C. M. Bishop. Pattern Recognition and Machine Learning. Springer Science+Business Media, 2006.
3. M. Black. Recursive non-linear estimation of discontinuous flow fields. In ECCV, pages 138-145, 1994.
4. T. Brox, A. Bruhn, N. Papenberg, and J. Weickert. High accuracy optical flow estimation based on a theory for warping. In ECCV, pages 25-36, 2004.
5. A. Bruhn, J.Weickert, and C. Schnörr. Lukas / kanade meets horn / schunk: Combining local and global optic flow methods. IJCV, 61(3):211-231, 2005.
6. P. Burgi, A.L.Yuille, and N. Grzywacz. Probabilistic motion estimation based on temporal coherence. Neural Computation, 12:1839-1867, 2000.
7. M. Elad and A. Feuer. Recursive optical flow estimation-adaptive filtering approach. Journal of Visual Communication and image representation, 9:119-138, 1998.
8. B. K. P. Horn and B. G. Schunk. Determining optic flow. Artificial Intelligence, 17:185-204, 1981.
9. B. D. Lukas and T. Kanade. An iterative image-registration technique with an application to stereo vision. In IJCAI, pages 674-679, 1981.
10. S. Roth and M. Black. On the spatial statistics of optical flow. In ICCV, pages 42-49, 2005.
11. E. Simoncelli. Handbook of Computer Vision and Applications, chapter Bayesian Multi-Scale Differential Optical Flow, pages 397-421. Academic Press, 1999.
12. E. Simoncelli, E. Adelson, and D. Heeger. Probability distributions of optical flow. In CVPR, pages 310-315, 1991.
13. Y. Weiss and D. Fleet. Velocity likelihoods in biological and machine vision. In Probabilistic Models of the Brain: Perception and Neural Function, pages 77-96. MIT Press, 2002.
14. J. Yedidia, W. Freeman, and Y. Weiss. Exploring Artificial Intelligence in the New Millennium, chapter Understanding Belief Propagation and Its Generalizations, pages 239-1236 Morgan Kaufmann, 2003.