The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Structure- and motion-adaptive regularization for high accuracy optic flow
Abstract
The accurate estimation of motion in image sequences is of central importance to numerous computer vision applications. Most competitive algorithms compute flow fields by minimizing an energy made of a data and a regularity term. To date, the best performing methods rely on rather simple purely geometric regularizes favoring smooth motion. In this paper, we revisit regularization and show that appropriate adaptive regularization substantially improves the accuracy of estimated motion fields. In particular, we systematically evaluate regularizes which adoptively favor rigid body motion (if supported by the image data) and motion field discontinuities that coincide with discontinuities of the image structure. The proposed algorithm relies on sequential convex optimization, is real-time capable and outperforms all previously published algorithms by more than one average rank on the Middlebury optic flow benchmark.
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Cites
A Duality Based Approach for Realtime TV-L 1 Optical Flow
Zach, C., Pock, T., & Bischof, H. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74936-3_22 · Cited by 1,658
Self-citationVariational Methods for Multimodal Image Matching
Hermosillo, G., Chefd'Hotel, C., & Faugeras, O. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020830525823 · Cited by 388
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Cited by
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
Self-citationA Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
EpicFlow: Edge-preserving interpolation of correspondences for optical flow
Revaud, J., Weinzaepfel, P., Harchaoui, Z., & Schmid, C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298720 · Cited by 697
Metadata
- Authors
- Wedel, A., Cremers, D., Pock, T., & Bischof, H.
- Venue
- 2009 IEEE 12th International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2009-09
- Pages
- 1663-1668
- OpenAlex
- W1977194745
- Cited by
- 177
- References
- 2
- FWCI
- 13.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wedel, A., Cremers, D., Pock, T., & Bischof, H. (2009). Structure- and motion-adaptive regularization for high accuracy optic flow. 2009 IEEE 12th International Conference on Computer Vision. https://doi.org/10.1109/iccv.2009.5459375
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