The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Motion estimation with non-local total variation regularization
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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Citations
Cites
Bilateral Filtering for Gray and Color Images
Tomasi, C., & Manduchi, R. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710815 · Cited by 8,177 · cited 1×
“This leads to the well known neighborhood filters [7] [20] [27] which belong to best performing methods.”
MethodA 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 · cited 2×
“To emphasize the potential of our approach we did some evaluations on the already introduced Middlebury benchmark database [3] and achieve top rankings for a lot of the evaluated metrics (cf . http://vision. middlebury.edu/flow).”
MethodA 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 · cited 3×
“After linearization, energy (2) yields a convex optimization problem which can be solved using for example the primal-dual quadratic splitting technique of [ 29 ].”
BackgroundMethodInfluentialSelf-citation
Cited by
Fast Cost-Volume Filtering for Visual Correspondence and Beyond
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., & Gelautz, M. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.156 · Cited by 673
Sun, D., Roth, S., & Black, M. J. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-013-0644-x · Cited by 614
Motion Detail Preserving Optical Flow Estimation
Xu, L., Jia, J., & Matsushita, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.236 · Cited by 493
Fast cost-volume filtering for visual correspondence and beyond
Rhemann, C., Hosni, A., Bleyer, M., Rother, C., & Gelautz, M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995372 · Cited by 485
Strasdat, H., Montiel, J. M. M., & Davison, A. J. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.009 · Cited by 439
Metadata
- Authors
- Werlberger, M., Pock, T., & Bischof, H.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 2464-2471
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2108565035
- Cited by
- 216
- References
- 3
- FWCI
- 20.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Werlberger, M., Pock, T., & Bischof, H. (2010). Motion estimation with non-local total variation regularization. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2010.5539945
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