This paper describes a hierarchical estimation framework for the computation of diverse representations of motion information. The key features of the resulting framework (or family of algorithms) are a global model that constrains the overall structure of the motion estimated, a local model that is used in the estimation process, and a coarse-fine refinement strategy. Four specific motion models: affine flow, planar surface flow, rigid body motion, and general optical flow, are described along with their application to specific examples.
CITATION STYLE
Bergen, J. R., Anandan, P., Hanna, K. J., & Hingorani, R. (1992). Hierarchical model-based motion estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 588 LNCS, pp. 237–252). Springer Verlag. https://doi.org/10.1007/3-540-55426-2_27
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