The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Large displacement optical flow
Abstract
The literature currently provides two ways to establish point correspondences between images with moving objects. On one side, there are energy minimization methods that yield very accurate, dense flow fields, but fail as displacements get too large. On the other side, there is descriptor matching that allows for large displacements, but correspondences are very sparse, have limited accuracy, and due to missing regularity constraints there are many outliers. In this paper we propose a method that can combine the advantages of both matching strategies. A region hierarchy is established for both images. Descriptor matching on these regions provides a sparse set of hypotheses for correspondences. These are integrated into a variational approach and guide the local optimization to large displacement solutions. The variational optimization selects among the hypotheses and provides dense and subpixel accurate estimates, making use of geometric constraints and all available image information.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
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 · cited 2×
“Most of the time is spent for the deformation of the patches and the variational flow, which is both potentially available in real-time using the GPU [19].”
Method
Research works printed but not linked: 18
- 10.1561/0600000017 · DOI · Paper page
- wills, 2004, A feature based method for determining dense long range correspondences, Proc ECCV, 3023, 170
- 10.1109/CVPR.2007.383205 · DOI
- strecha, 2004, A propabilistic approach to large displacement optical flow and occlusion detection, Statistical Methods in Video Processing, 3247, 71, 10.1007/978-3-540-30212-4_7 · DOI
- 10.1016/j.imavis.2004.02.006 · DOI · Paper page
- 10.1109/83.668027 · DOI · Paper page
- 10.1109/CVPR.2008.4587420 · DOI · Paper page
- 10.1109/TPAMI.2004.1273918 · DOI · Paper page
- 10.1006/cviu.1996.0006 · DOI · Paper page
- 10.1109/ICCV.2005.61 · DOI
- baker, 2007, A database and evaluation methodology for optical flow, Proc ICCV
- 10.1109/CVPR.2009.5206707 · DOI · Paper page
- lucas, 1981, An iterative image registration technique with an application to stereo vision, Proc Seventh International Joint Conference on Artificial Intelligence, 674
- laptev, 2004, Local Spatio-temporal Image Features for Motion Interpretation
- 10.1016/0004-3702(81)90024-2 · DOI · Paper page
- 10.1109/ICCV.2003.1238420 · DOI · Paper page
- brox, 2004, High accuracy optical flow estimation based on a theory for warping, Proc ECCV, 25
- liu, 2008, SIFT flow: Dense correspondence across different scenes, Proc ECCV, 28
Cited by
(2020) · doi:10.1007/978-3-030-58536-5_24 · Cited by 2,824
A 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
“Such sparse matching method can be combined with the continuous energy minimization approaches in a variety of ways ( Brox et al. 2009; Liu et al. 2008 ;R en2008; Xu et al. 2008).”
MethodLarge Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295
Self-citationModeling Actions through State Changes
Fathi, A., & Rehg, J. M. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.333 · Cited by 145
Optical Flow Estimation with Deep Learning, a Survey on Recent Advances
Savian, S., Elahi, M., & Tillo, T. (2020) · Unsupervised and Semi-Supervised Learning · doi:10.1007/978-3-030-32583-1_12 · Cited by 29
Metadata
- Authors
- Brox, T., Bregler, C., & Malik, J.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 41-48
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2171857946
- Cited by
- 277
- References
- 2
- FWCI
- 20.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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