The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Robust <formula formulatype="inline"><tex Notation="TeX">$L_{2}E$</tex> </formula> Estimation of Transformation for Non-Rigid Registration
Abstract
We introduce a new transformation estimation algorithm using the L2E estimator and apply it to non-rigid registration for building robust sparse and dense correspondences. In the sparse point case, our method iteratively recovers the point correspondence and estimates the transformation between two point sets. Feature descriptors such as shape context are used to establish rough correspondence. We then estimate the transformation using our robust algorithm. This enables us to deal with the noise and outliers which arise in the correspondence step. The transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. In the dense point case for nonrigid image registration, our approach consists of matching both sparsely and densely sampled SIFT features, and it has particular advantages in handling significant scale changes and rotations. The experimental results show that our approach greatly outperforms state-of-the-art methods, particularly when the data contains severe outliers.
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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
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Image registration methods: a survey
Zitová, B., & Flusser, J. (2003) · Image and Vision Computing · doi:10.1016/s0262-8856(03)00137-9 · Cited by 6,423
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
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
A comparison of affine region detectors
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & Gool, L. V. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-3848-x · Cited by 2,939
A new point matching algorithm for non-rigid registration
Chui, H., & Rangarajan, A. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00009-2 · Cited by 1,560
Kim, V. G., Lipman, Y., & Funkhouser, T. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964974 · Cited by 298
Cited by
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
Self-citationA review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 532
Self-citationNon-Rigid Point Set Registration by Preserving Global and Local Structures
Ma, J., Zhao, J., & Yuille, A. L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2467217 · Cited by 334
Self-citationGuided 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
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Metadata
- Authors
- Ma, J., Qiu, W., Zhao, J., Ma, Y., Yuille, A. L., & Tu, Z.
- Venue
- IEEE Transactions on Signal Processing
- Published
- 2015-03
- Volume
- 63
- Issue
- 5
- Pages
- 1115-1129
- ISSN
- 1053-587X (ISSN-L)
- 1941-0476
- OpenAlex
- W2157656099
- Cited by
- 280
- References
- 9
- FWCI
- 38.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ma, J., Qiu, W., Zhao, J., Ma, Y., Yuille, A. L., & Tu, Z. (2015). Robust <formula formulatype="inline"><tex Notation="TeX">$L_{2}E$</tex> </formula> Estimation of Transformation for Non-Rigid Registration. IEEE Transactions on Signal Processing. https://doi.org/10.1109/tsp.2014.2388434
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