The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Feature Correspondence Via Graph Matching: Models and Global Optimization
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Citations
Cites
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 11,726
Network Flows: Theory, Algorithms, and Applications.
Smith, D. K., Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1994) · The Journal of the Operational Research Society · doi:10.2307/2583863 · Cited by 8,034
Shape matching and object recognition using shape contexts
Belongie, S., Malik, J., & Puzicha, J. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.993558 · Cited by 6,306
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
Minimization Methods for Non-Differentiable Functions
Shor, N. Z. (1985) · Springer Series in Computational Mathematics · doi:10.1007/978-3-642-82118-9 · Cited by 1,551
The pyramid match kernel: discriminative classification with sets of image features
Grauman, K., & Darrell, T. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.239 · Cited by 1,481
THIRTY YEARS OF GRAPH MATCHING IN PATTERN RECOGNITION
CONTE, D., FOGGIA, P., SANSONE, C., & VENTO, M. (2004) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001404003228 · Cited by 1,370
A spectral technique for correspondence problems using pairwise constraints
Leordeanu, M., & Hebert, M. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.20 · Cited by 1,222
Computing visual correspondence with occlusions using graph cuts
Kolmogorov, V., & Zabih, R. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937668 · Cited by 1,119
Self-citation
Cited by
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587
“also employed pairwise constraints to model local spatial coherence [289].”
MethodMa, J., Zhao, J., Jiang, J., Zhou, H., & Guo, X. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-018-1117-z · Cited by 601
Robust Point Matching via Vector Field Consensus
Ma, J., Zhao, J., Tian, J., Yuille, A. L., & Tu, Z. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2307478 · Cited by 566
Guided Locality Preserving Feature Matching for Remote Sensing Image Registration
Ma, J., Jiang, J., Zhou, H., Zhao, J., & Guo, X. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2820040 · Cited by 283
Minimum-Distortion Isometric Shape Correspondence Using EM Algorithm
Sahillioglu, Y., & Yemez, Y. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.26 · Cited by 53
Metadata
- Authors
- Torresani, L., Kolmogorov, V., & Rother, C.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 596-609
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1744214816
- Cited by
- 388
- References
- 35
- FWCI
- 20.03
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Feature Correspondence Via Graph Matching: Models and Global Optimization. https://app.cochat.ai/papers/10.1007/978-3-540-88688-4_44
- The paper
- Torresani, L., Kolmogorov, V., & Rother, C. (2008). Feature Correspondence Via Graph Matching: Models and Global Optimization. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-88688-4_44
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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.
