| Authors |
Trobin Werner, Pock Thomas, Daniel Cremers, Bischof Horst |
| Appeared in |
Proceedings of the 30th DAGM Symposium on Pattern Recognition |
| Publisher |
Springer LNCS, |
| Organization |
Deutsche Arbeitsgemeinschaft für Mustererkennung |
| Date |
June 2008 |
| Abstract |
Virtually all variational methods for motion estimation regularize the gradient of the
flow field, which introduces a bias towards piecewise constant motions in weakly
textured areas. We propose a novel regularization approach, based on decorrelated
second-order derivatives, that does not suffer from this shortcoming. We then derive an
efficient numerical scheme to solve the new model using projected gradient descent. A
comparison to a TV regularized model shows that the proposed second-order prior exhibits
superior performance, in particular in low-textured areas (where the prior becomes
important). Finally, we show that the proposed model yields state-of-the-art results on
the Middlebury optical flow database. |
| Link |
PDF
|
Semi-Supervised Learning in Vision, CVPR 2010 tutorial
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