The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Highly Accurate Optic Flow Computation with Theoretically Justified Warping
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Citations
Cites
Nonlinear total variation based noise removal algorithms
Rudin, L. I., Osher, S., & Fatemi, E. (1992) · Physica D: Nonlinear Phenomena · doi:10.1016/0167-2789(92)90242-f · Cited by 15,704
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Horn, B. K., & Schunck, B. G. (1981) · SPIE Proceedings · doi:10.1117/12.965761 · Cited by 7,394
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Performance of optical flow techniques
Barron, J. L., Fleet, D. J., & Beauchemin, S. S. (1994) · International Journal of Computer Vision · doi:10.1007/bf01420984 · Cited by 4,305
Robust Statistics: The Approach Based on Influence Functions
Ruppert, D. (1987) · Technometrics · doi:10.2307/1269782 · Cited by 3,880
High Accuracy Optical Flow Estimation Based on a Theory for Warping
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24673-2_3 · Cited by 2,570
Self-citationIterative Solution of Large Linear Systems
(1971) · Elsevier eBooks · doi:10.1016/c2013-0-11733-3 · Cited by 1,734
The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields
Black, M. J., & Anandan, P. (1996) · Computer Vision and Image Understanding · doi:10.1006/cviu.1996.0006 · Cited by 1,570
Cited by
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
Large 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-citationLiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation
Hui, T.-W., Tang, X., & Loy, C. C. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00936 · Cited by 817
A Database and Evaluation Methodology for Optical Flow
Baker, S., Roth, S., Scharstein, D., Black, M. J., Lewis, J. P., & Szeliski, R. (2007) · 2007 IEEE 11th International Conference on Computer Vision · doi:10.1109/iccv.2007.4408903 · Cited by 618
FusionFlow: Discrete-continuous optimization for optical flow estimation
Lempitsky, V., Roth, S., & Rother, C. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587751 · Cited by 145
Metadata
- Authors
- Papenberg, N., Bruhn, A., Brox, T., Didas, S., & Weickert, J.
- Venue
- International Journal of Computer Vision
- Published
- 2006-01-28
- Volume
- 67
- Issue
- 2
- Pages
- 141-158
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2168874984
- Cited by
- 409
- References
- 48
- FWCI
- 36.85
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Papenberg, N., Bruhn, A., Brox, T., Didas, S., & Weickert, J. (2006). Highly Accurate Optic Flow Computation with Theoretically Justified Warping. International Journal of Computer Vision. https://doi.org/10.1007/s11263-005-3960-y
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
