Building a motion resolution pyramid by combining velocity distributions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3175, Issue , 2004, Pages 310-317

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

^a HONDA RESEARCH INSTITUTE EUROPE GMBH   (Germany)

^b DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

PATTERN RECOGNITION; VELOCITY;

APERTURE PROBLEMS; COARSE-TO-FINE STRATEGY; DIFFERENT RESOLUTIONS; INTEGRATING INFORMATION; MOTION INFORMATION; MOTION RESOLUTION; VELOCITY INFORMATION; VELOCITY VECTORS;

VELOCITY DISTRIBUTION;

EID: 33749415444     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-28649-3_38     Document Type: Article

References (5)

1. Handbook of Computer Vision and Applications, chapter Bayesian Multi-Scale Differential Optical Flow. Academic Press, 1999.
2. J. Bergen, P. Anandan, K. Hanna, and R. Hingorani. Hierarchical model-based motion estimation. In Proc. 2nd Europ Conf Comput Vis, 1992.
3. E. Simoncelli, E.H. Adelson, and D.J. Heeger. Probability distributions of optical flow. In Proc Conf Comput Vis and Pat Recogn. IEEE Computer Society, 1991.
4. Y. Weiss and D.J. Fleet. Velocity likelihoods in biological and machine vision. In Probabilistic Models of the Brain: Perception and Neural Function. MIT Press, 2002.
5. Qing X. Wu. A correlation-relaxation-labeling framework for computing optical flow - template matching from a new perspective. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17, 1995.