2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2008)
Anchorage, AK, USA
June 23, 2008 to June 28, 2008
Dan Koppel , Department of Computer Science, University of California, Santa Barbara, USA
Chang-Ming Tsai , Department of Computer Science, University of California, Santa Barbara, USA
Yuan-Fang Wang , Department of Computer Science, University of California, Santa Barbara, USA
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 ∇u∇u<sup>T</sup> + ∇v∇v<sup>T</sup> . 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.
Dan Koppel, Chang-Ming Tsai, Yuan-Fang Wang, "Regularizing optical-flow computation using tensor theory and complex analysis", 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 00, no. , pp. 1-6, 2008, doi:10.1109/CVPRW.2008.4562971