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Regularizing optical-flow computation using tensor theory and complex analysis

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3 Author(s)
Koppel, D. ; Dept. of Comput. Sci., Univ. of California, Santa Barbara, CA ; Chang-Ming Tsai ; Yuan-Fang Wang

This paper reports a technique that improves the robustness and accuracy in computing dense optical-flow fields. We propose a global formulation with a regularization term. The regularization expressions are derived based on tensor theory and complex analysis. It is shown that while many regularizers have been proposed (image-driven, flow-driven, homogeneous, inhomogeneous, isotropic, anisotropic), they are all variations of a single base expression nablaunablauT + nablavnablavT . These regularizers, strictly speaking, are valid for uniform 2D translational motion only, because what they do essentially is to penalize changes in a flow field. However, many flow patterns-such as rotation, zoom, and their combinations, induced by a 3D rigid-body motion.are not constant. The traditional regularizers then incorrectly penalize these legal flow patterns and result in biased estimates. The purpose of this work is then to derive a new suite of regularization expressions that treat all valid flow patterns resulting from a 3D rigid-body motion equally, without unfairly penalizing any of them.

Published in:

Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on

Date of Conference:

23-28 June 2008