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Motion Estimation with Non-Local Total Variation Regularization

Manuel Werlberger, Thomas Pock, Horst Bischof
CVPR 2010 pp. 2464-2471
doi:10.1109/CVPR.2010.5539945 /cvpr/2010/werlberger2010cvpr-motion/

Abstract

State-of-the-art motion estimation algorithms suffer from three major problems: Poorly textured regions, occlusions and small scale image structures. Based on the Gestalt principles of grouping we propose to incorporate a low level image segmentation process in order to tackle these problems. Our new motion estimation algorithm is based on non-local total variation regularization which allows us to integrate the low level image segmentation process in a unified variational framework. Numerical results on the Middlebury optical flow benchmark data set demonstrate that we can cope with the aforementioned problems.

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Cite

Text

Werlberger et al. "Motion Estimation with Non-Local Total Variation Regularization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539945

Markdown

[Werlberger et al. "Motion Estimation with Non-Local Total Variation Regularization." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/werlberger2010cvpr-motion/) doi:10.1109/CVPR.2010.5539945

BibTeX

@inproceedings{werlberger2010cvpr-motion,
  title     = {{Motion Estimation with Non-Local Total Variation Regularization}},
  author    = {Werlberger, Manuel and Pock, Thomas and Bischof, Horst},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2010},
  pages     = {2464-2471},
  doi       = {10.1109/CVPR.2010.5539945},
  url       = {https://mlanthology.org/cvpr/2010/werlberger2010cvpr-motion/}
}