GIBBS RANDOM-FIELD MODEL-BASED 3-D MOTION ESTIMATION FROM VIDEO SEQUENCES

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1994-01-01

Author

ONURAL, L
Alatan, Abdullah Aydın

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In contrast to previous global 3-D motion concept, a Gibbs random field based method, which models local interactions between motion parameters defined at each point on the object, is proposed. Anenergy function which gives the joint probability distribution of motion vectors, is constructed. Theenergy function is minimized in order to find the most likely motion vector set. Some convergenceproblems, due to ill-posedness of the problem, are overcome by using the concept of hierarchical rigidity. In hierarchical rigidity, the objects are assumed to he almost rigid in the coarsest level andthis rigidness is weakened at each level until the finest level is reached. The propagation of motioninformation between levels, is encouraged. At the finest level, each point have a motion vector associated with it and the interaction between these vectors are described by the energy function. Theminimization of the energy function is achieved by using hierarchical rigidity, without trapping intoa local minimum. The results are promising.

Subject Keywords

3-D motion estimation, Gibbs random field modelling, Hierarchical rigidity, Non-rigidity

URI

https://hdl.handle.net/11511/39225

DOI

https://doi.org/10.1117/12.186006

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

L. ONURAL and A. A. Alatan, “GIBBS RANDOM-FIELD MODEL-BASED 3-D MOTION ESTIMATION FROM VIDEO SEQUENCES,” 1994, vol. 2308, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39225.