The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
A new algorithm for non-rigid point matching
Abstract
We present a new robust point matching algorithm (RPM) that can jointly estimate the correspondence and non-rigid transformations between two point-sets that may be of different sizes. The algorithm utilizes the soft assign for the correspondence and the thin-plate spline for the non-rigid mapping. Embedded within a deterministic annealing framework, the algorithm can automatically reject a fraction of the points as outliers. Experiments on both 2D synthetic point-sets with varying degrees of deformation, noise and outliers, and on real 3D sulcal point-sets (extracted from brain MRI) demonstrate the robustness of the algorithm.
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Cites
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Principal warps: thin-plate splines and the decomposition of deformations
Bookstein, F. L. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.24792 · Cited by 4,913
Spline Models for Observational Data
Wahba, G. (1990) · doi:10.1137/1.9781611970128 · Cited by 4,184
A graduated assignment algorithm for graph matching
Gold, S., & Rangarajan, A. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.491619 · Cited by 1,090
Self-citationNew algorithms for 2D and 3D point matching
Gold, S., Rangarajan, A., Lu, C.-P., Pappu, S., & Mjolsness, E. (1998) · Pattern Recognition · doi:10.1016/s0031-3203(98)80010-1 · Cited by 580
Self-citationStructural Descriptions and Inexact Matching
Shapiro, L. G., & Haralick, R. M. (1981) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1981.4767144 · Cited by 544
Modal matching for correspondence and recognition
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Parallel and deterministic algorithms from MRFs: surface reconstruction
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An algorithm for associating the features of two images
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Cited by
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
Point Set Registration: Coherent Point Drift
Myronenko, A., & Song, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.46 · Cited by 2,762
Computational Optimal Transport
Peyré, G., & Cuturi, M. (2019) · now publishers, Inc. eBooks · doi:10.1561/9781680835519 · Cited by 847
Efficient shape matching using shape contexts
Mori, G., Belongie, S., & Malik, J. (2005) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2005.220 · Cited by 458
The dual-bootstrap iterative closest point algorithm with application to retinal image registration
Stewart, C. V., Tsai, C.-L., & Roysam, B. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.819276 · Cited by 346
Metadata
- Authors
- Chui, H., & Rangarajan, A.
- Venue
- Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Volume
- 2
- Pages
- 44-51
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2096840836
- Cited by
- 440
- References
- 23
- FWCI
- 53.67
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chui, H., & Rangarajan, A. (2002). A new algorithm for non-rigid point matching. Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662). https://doi.org/10.1109/cvpr.2000.854733
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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.
