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Fast piecewise-affine motion estimation without segmentation| old_uid | 16927 |
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| title | Fast piecewise-affine motion estimation without segmentation |
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| start_date | 2018/12/06 |
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| schedule | 15h-16h |
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| online | no |
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| location_info | salle 314 |
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| summary | In this talk, we will review existing strategies for regularizing motion fields, and present a new method dedicated to piecewise affine models. Current algorithmic approaches for piecewise affine motion estimation are based on alternating motion segmentation and estimation. In contrast, our method estimates piecewise affine motion directly without intermediate segmentation. To this end, we reformulate the problem by imposing piecewise constancy of the parameter field, and derive a specific proximal splitting optimization scheme. A key component of our framework is an efficient 1D piecewise-affine estimator for vector-valued signals. The first advantage of our approach over segmentation-based methods is its absence of initialization. The second advantage is its lower computational cost, which is independent of the complexity of the motion field. In addition to these features, we demonstrate competitive accuracy with other piecewise-parametric methods on standard evaluation benchmarks. Our new regularization scheme also outperforms the more standard use of total variation and total generalized variation. |
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| responsibles | Almansa, Delon |
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