Data-Driven Motion Estimation with Spatial Adaptation

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Provided by: Computer Science Journals
Topic: Software
Format: PDF
Besides being an ill-posed problem, the pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. The authors' proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalized Cross Validation (GCV) to estimate the best regularization scheme for a given moving pixel. In their model, a regularization matrix carries information about different sources of error in its entries and motion vector estimation takes into consideration local image properties following a spatially adaptive. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
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