The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration
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Citations
Cites
A method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 18,103
Medical image analysis: progress over two decades and the challenges ahead
Duncan, J. S., & Ayache, N. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824822 · Cited by 4,140
A pyramid approach to subpixel registration based on intensity
Thevenaz, P., Ruttimann, U. E., & Unser, M. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.650848 · Cited by 3,078
Iterative point matching for registration of free-form curves and surfaces
Zhang, Z. (1994) · International Journal of Computer Vision · doi:10.1007/bf01427149 · Cited by 2,402
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Scale-Space Theory in Computer Vision
Lindeberg, T. (1994) · Springer eBooks · doi:10.1007/978-1-4757-6465-9 · Cited by 2,169
Estimating 3-D rigid body transformations: a comparison of four major algorithms
Eggert, D. W., Lorusso, A., & Fisher, R. B. (1997) · Machine Vision and Applications · doi:10.1007/s001380050048 · Cited by 804
Statistical Optimization for Geometric Computation: Theory and Practice
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A Robust Method for Registration and Segmentation of Multiple Range Images
Masuda, T., & Yokoya, N. (1995) · Computer Vision and Image Understanding · doi:10.1006/cviu.1995.1024 · Cited by 250
Registration and integration of multiple range images for 3-D model construction
Masuda, T., Sakaue, K., & Yokoya, N. (1996) · Proceedings of 13th International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546150 · Cited by 197
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
“Pennec et al. [23] dropped the linearity assumption by modeling the deformation process through the St Venant-Kirchoff elasticity energy that extends the linear elastic model to the nonlinear regime.”
MethodInfluentialImage 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
TEASER: Fast and Certifiable Point Cloud Registration
Yang, H., Shi, J., & Carlone, L. (2021) · IEEE Transactions on Robotics · doi:10.1109/tro.2020.3033695 · Cited by 895
A 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
A Correlation-Based Approach to Robust Point Set Registration
Tsin, Y., & Kanade, T. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24672-5_44 · Cited by 416
Metadata
- Authors
- Granger, S., & Pennec, X.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2002
- Pages
- 418-432
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1599434464
- Cited by
- 444
- References
- 20
- FWCI
- 9.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration. https://app.cochat.ai/papers/10.1007/3-540-47979-1_28
- The paper
- Granger, S., & Pennec, X. (2002). Multi-scale EM-ICP: A Fast and Robust Approach for Surface Registration. Lecture Notes in Computer Science. https://doi.org/10.1007/3-540-47979-1_28
Data sources
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.
