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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-6
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
ABSTRACT
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.
CITATION
Dan Koppel, Chang-Ming Tsai, Yuan-Fang Wang, "Regularizing optical-flow computation using tensor theory and complex analysis", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-6, doi:10.1109/CVPRW.2008.4562971
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