Authors:
Tarik Arici
and
Vural Aksakalli
Affiliation:
Istanbul Sehir University, Turkey
Keyword(s):
Energy Minimization, Approximation Algorithms, Primal-dual Method, Motion Estimation.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
Abstract:
Energy minimization algorithms are used in low-level computer vision applications for labeling tasks such as stereo-disparity estimation, image restoration, motion estimation, and optical flow. The energy function involves terms that evaluate the goodness of a solution in terms of a prior knowledge in addition to data terms. The most widely used priors are smoothness-based priors, which enhance the quality significantly. However, the smoothness assumption is not valid across discontinuities (e.g. motion boundaries). We present a method to update the weights of smoothness terms using the dual problem when the approximation algorithm is iterative. The dual of the primal energy minimization problem is used to infer about the validity of the smoothness prior and impose it more correctly at each iteration. We demonstrate the effectiveness of this method against the state-of-the-art in the optical flow literature.