Performance of optical flow techniques
International Journal of Computer Vision
Reliable Estimation of Dense Optical Flow Fields with Large Displacements
International Journal of Computer Vision
A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion
International Journal of Computer Vision
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
International Journal of Computer Vision
Robust optic-flow estimation with bayesian inference of model and hyper-parameters
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
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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.