The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Equivalence and efficiency of image alignment algorithms
Abstract
There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additive approach), the second an incremental warp (the compositional approach). We first prove that these two formulations are equivalent. A very efficient algorithm was proposed by Hager and Belhumeur (1998) using the additive approach that unfortunately can only be applied to a very restricted class of warps. We show that using the compositional approach an equally efficient algorithm (the inverse compositional algorithm) can be derived that can be applied to any set of warps which form a group. While most warps used in computer vision form groups, there are a certain warps that do not. Perhaps most notable is the set of piecewise affine warps used in flexible appearance models (FAMs). We end this paper by extending the inverse compositional algorithm to apply to FAMs.
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Cites
(1981) · Cited by 11,963
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0054760 · Cited by 2,195
Hierarchical model-based motion estimation
Bergen, J. R., Anandan, P., Hanna, K. J., & Hingorani, R. (1992) · Lecture Notes in Computer Science · doi:10.1007/3-540-55426-2_27 · Cited by 1,154
Efficient region tracking with parametric models of geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.722606 · Cited by 1,066
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
Black, M. J., & Jepson, A. D. (1998) · International Journal of Computer Vision · doi:10.1023/a:1007939232436 · Cited by 1,029
La Cascia, M., Sclaroff, S., & Athitsos, V. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.845375 · Cited by 566
Construction of Panoramic Image Mosaics with Global and Local Alignment
Shum, H.-Y., & Szeliski, R. (2001) · Monographs in Computer Science · doi:10.1007/978-1-4757-3482-9_13 · Cited by 356
Sclaroff, S., & Isidoro, J. R. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1998.710860 · Cited by 145
Sclaroff, S., & Isidoro, J. R. (1998) · International Conference on Computer Vision · Cited by 139
Fast Image-Based Tracking by Selective Pixel Integration
Dellaert, F. (2011) · Cited by 81
Projective registration with difference decomposition
Gleicher, M. L. (2002) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.1997.609345 · Cited by 79
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Metadata
- Authors
- Baker, S., & Matthews, I.
- Venue
- Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001
- Publisher
- IEEE Comput. Soc
- Published
- 2005-08-24
- Volume
- 1
- Pages
- I-1090-I-1097
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2096503284
- Cited by
- 406
- References
- 11
- FWCI
- 24.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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