The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
SIFT Flow: Dense Correspondence across Different Scenes
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Citations
Cites
Object recognition from local scale-invariant features
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Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
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A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Research works printed but not linked: 19
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- Horn, B.K.P., Schunck, B.G.: Determing optical flow. Artificial Intelligence 17, 185–203 (1981) · DOI · Paper page
- Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)
- Bruhn, A., Weickert, J., Schnörr, C.: Lucas/Kanade meets Horn/Schunk: combining local and global optic flow methods. Intl. J. of Computer Vision 61(3), 211–231 (2005) · DOI · Paper page
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Cited by
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Eigen, D., Puhrsch, C., & Fergus, R. (2014) · arXiv · doi:10.48550/arxiv.1406.2283 · Cited by 3,442
Eigen, D., & Fergus, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.304 · Cited by 2,862
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
“For example a Field-of-Experts formulation was used in [70] and SIFT features were used in [43].”
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
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., & Zhao, H. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.00987 · Cited by 1,296
Metadata
- Authors
- Liu, C., Yuen, J., Torralba, A., Sivic, J., & Freeman, W. T.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 28-42
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1230023165
- Cited by
- 540
- References
- 5
- FWCI
- 24.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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- Liu, C., Yuen, J., Torralba, A., Sivic, J., & Freeman, W. T. (2008). SIFT Flow: Dense Correspondence across Different Scenes. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-88690-7_3
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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