Uncertainty optimization for robust dynamic optical flow estimation

Proceedings - 6th International Conference on Machine Learning and Applications, ICMLA 2007

Volumn , Issue , 2007, Pages 450-457

  Willert, Volker ^a   Toussaint, Marc ^a,b   Eggert, Julian ^b   Körner, Edgar ^b  

^a HRI Europe GmbH   (Germany)

^b TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; ESTIMATION; INFERENCE ENGINES; LEARNING SYSTEMS; MOTION ESTIMATION; OPTIMIZATION; PARAMETER ESTIMATION; ROBOT LEARNING; STRUCTURAL OPTIMIZATION; UNCERTAINTY ANALYSIS;

BAYESIAN PROPAGATION; CAMERA NOISE; DISTRIBUTED MODELLING; DYNAMIC BAYESIAN NETWORK (DBN); FLOW EVOLUTION; INFERENCE METHODS; INTERNATIONAL CONFERENCES; MACHINE-LEARNING; MOTION DISCONTINUITIES; MOTION INFORMATION; NOISY DATA; OPTICAL (PET) (OPET); OPTICAL FLOW ESTIMATION; PARAMETER OPTIMIZATION; PROBABILISTIC FRAMEWORKS; SPATIAL AND TEMPORAL COHERENCE; SYNTHETIC SEQUENCES; TRANSITION NOISE;

OPTICAL FLOWS;

EID: 47349084671     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2007.110     Document Type: Conference Paper

References (15)

1. J. Barron, D. Fleet, and S. Beauchemin. Performance of optical flow techniques. IJCV, 12(1):43-77, 1994.
2. M. Black and P. Anandan. Robust dynamic motion estimation over time. In CVPR, pages 296-302, Maui, Hawaii, 1991.
3. 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.
4. P. Burgi, A.L.Yuille, and N. Grzywacz. Probabilistic motion estimation based on temporal coherence. Neural Computation, 12(8): 1839-1867, 2000.
5. F. Heitz and P. Bouthemy. Multimodal estimation of discontinuous optical flow using markov random fields. PAMI, 15(12): 1217-1232, 1993.
6. G. Kitagawa. The two-filter formula for smoothing and an implementation of the gaussian-sum smoother. Annals Institute of Statistical Mathematics, 46(4):605-623, 1994.
7. K. Krajsek and R. Mester. A maximum likelihood estimator for choosing the regularizad on parameters in global optical flow methods. In ICIP, pages 1081-1084, Atlanta, USA, 2006.
8. K. Lim, A. Das, and M. Chong. Estimation of occlusion and dense motion fields in a bidirectional bayesian framework. PAMI, 24(5):712-718, 2002.
9. K. Murphy. Dynamic bayesian network: Representation, inference and learning. PhD Thesis, UC Berkeley, Computer Science Division, 2002.
10. S. Roth and M. Black. On the spatial statistics of optical flow. In ICCV, pages 42-49, Beijing, China, 2005.
11. E. Simoncelli, E. Adelson, and D. Heeger. Probability distributions of optical flow. In CVPR, pages 310-315, Maui, Hawaii, 1991.
12. A. Singh. Incremental estimation of image flow using a kalman filter. In IEEE Workshop on Visual Motion, pages 36-43, Princeton, USA, 1991.
13. A. A. Stocker and E. P. Simoncelli. Noise characteristics and prior expectations in human visual speed perception. Nature Neuroscience, 9(4):578-585, 2006.
14. 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.
15. V. Willert, J. Eggert, J. Adamy, and E. Körner. Non-gaussian velocity distributions integrated over space, time, and scales. SMCB - Part B, 36(3):482-493, 2006.