Motion Estimation via Belief Propagation

Motion Estimation via Belief Propagation

We present a probabilistic model for motion estimation in which motion characteristics are inferred on the basis of a finite mixture of motion models. The model is graphically represented in the form of a pairwise Markov random field network upon which a Loopy belief propagation algorithm is exploit...

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Bibliographic Details
Published in:14th International Conference on Image Analysis and Processing (ICIAP 2007) pp. 55 - 60
Main Authors: Boccignone, G., Marcelli, A., Napoletano, P., Ferraro, M.
Format: Conference Proceeding
Language:English
Published: IEEE 01-09-2007
Subjects:
Belief propagation
Graphical models
Inference algorithms
Layout
Markov random fields
Motion estimation
Random variables
Video sequences
Visual system
Yttrium
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Description
Summary:We present a probabilistic model for motion estimation in which motion characteristics are inferred on the basis of a finite mixture of motion models. The model is graphically represented in the form of a pairwise Markov random field network upon which a Loopy belief propagation algorithm is exploited to perform inference. Experiments on different video clips are presented and discussed.
ISBN:0769528775
9780769528779
DOI:10.1109/ICIAP.2007.4362757