Abstract
Global optical flow techniques minimize a mixture of two terms: a data term relating the observable signal with the optical flow, and a regularization term imposing prior knowledge/assumptions on the solution. A large number of different data terms have been developed since the first global optical flow estimator proposed by Horn and Schunk [1]. Recently [2], these data terms have been classified with respect to their properties. Thus, for image sequences where certain properties about image as well as motion characteristics are known in advance, the appropriate data term can be chosen from this classification. In this contribution, we deal with the situation where the optimal data term is not known in advance. We apply the Bayesian evidence framework for automatically choosing the optimal relative weight between two data terms as well as the regularization term based only on the given input signal.
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Krajsek, K., Mester, R. (2007). Bayesian Model Selection for Optical Flow Estimation. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds) Pattern Recognition. DAGM 2007. Lecture Notes in Computer Science, vol 4713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74936-3_15
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DOI: https://doi.org/10.1007/978-3-540-74936-3_15
Publisher Name: Springer, Berlin, Heidelberg
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