Groupwise point pattern registration using a novel CDF-based Jensen-Shannon Divergence
Open accessAbstract
In this paper, we propose a novel and robust algorithm for the groupwise non-rigid registration of multiple unlabeled point-sets with no bias toward any of the given point-sets. To quantify the divergence between multiple probability distributions each estimated from the given point sets, we develop a novel measure based on their cumulative distribution functions that we dub the CDF-JS divergence. The measure parallels the well known Jensen-Shannon divergence (defined for probability density functions) but is more regular than the JS divergence since its definition is based on CDFs as opposed to density functions. As a consequence, CDF-JS is more immune to noise and statistically more robust than the JS.We derive the analytic gradient of the CDF-JS divergence with respect to the non-rigid registration parameters for use in the numerical optimization of the groupwise registration leading a computationally efficient and accurate algorithm. The CDF-JS is symmetric and has no bias toward any of the given point-sets, since there is NO fixed reference data set. Instead, the groupwise registration takes place between the input data sets and an evolving target dubbed the pooled model. This target evolves to a fully registered pooled data set when the CDF-JS defined over this pooled data is minimized. Our algorithm is especially useful for creating atlases of various shapes (represented as point distribution models) as well as for simultaneously registering 3D range data sets without establishing any correspondence. We present experimental results on non-rigid registration of 2D/3D real point set data.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Groupwise registration techniques have been developed [33], [123] which are typically more reliable, and reduce the accumulated errors that arise in pairwise registration.
“Groupwise registration techniques have been developed [33], [123] which are typically more reliable, and reduce the accumulated errors that arise in pairwise registration.”
From Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid (2013) · cites this paper as [123]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163 · see citing works
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 · see citing works
Self-citationRobust registration of 2D and 3D point sets
Fitzgibbon, A. W. (2003) · Image and Vision Computing · doi:10.1016/j.imavis.2003.09.004 · Cited by 925 · see citing works
A new algorithm for non-rigid point matching
Chui, H., & Rangarajan, A. (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.2000.854733 · Cited by 443 · see citing works
Self-citationA Correlation-Based Approach to Robust Point Set Registration
Tsin, Y., & Takeo Kanade, A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24672-5_44 · Cited by 424 · see citing works
A robust algorithm for point set registration using mixture of Gaussians
Jian, B., & Vemuri, B. C. (2005) · doi:10.1109/iccv.2005.17 · Cited by 361 · see citing works
Self-citationGlaunès, J., Trouvé, A., & Laurent Younes, B. (2004) · 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.2004.1315234 · Cited by 168 · see citing works
Jumarie, G. (1990) · Springer series in synergetics · doi:10.1007/978-3-642-84017-3 · Cited by 108 · see citing works
Unsupervised learning of an atlas from unlabeled point-sets
Chui, H., Rangarajan, A., Zhang, J., & Leonard, C. M. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1262178 · Cited by 103 · see citing works
Self-citationA New & Robust Information Theoretic Measure and Its Application to Image Alignment
Wang, F., Vemuri, B. C., Rao, M., & Chen, Y. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45087-0_33 · Cited by 49 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Registration of 3D Point Clouds and Meshes: A Survey from Rigid to Nonrigid
Tam, G. K. L., Cheng, Z.-Q., Lai, Y.-K., Langbein, F. C., Liu, Y., Marshall, D., Martin, R. R., Sun, X.-F., & Rosin, P. L. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2012.310 · Cited by 679 · see citing works
A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration
Zhu, H., Guo, B., Zou, K., Li, Y., Yuen, K., Mihaylova, L. S., & Leung, H. (2019) · Sensors · doi:10.3390/s19051191 · Cited by 99 · see citing works
Segmenting the prostate and rectum in CT imagery using anatomical constraints
Chen, S., Lovelock, D. M., & Radke, R. J. (2010) · Medical Image Analysis · doi:10.1016/j.media.2010.06.004 · Cited by 76 · see citing works
Geng, X., Christensen, G. E., Gu, H., Ross, T. J., & Yang, Y. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.04.024 · Cited by 75 · see citing works
Group-Wise Registration of Point Sets for Statistical Shape Models
Rasoulian, A., Rohling, R. N., & Abolmaesumi, P. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2202913 · Cited by 73 · see citing works
Robust thermal infrared tracking via an adaptively multi-feature fusion model
Yuan, D., Shu, X., Liu, Q., Zhang, X., & He, Z. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07867-1 · Cited by 55 · see citing works
Group-Wise Point-Set Registration Using a Novel CDF-Based Havrda-Charvát Divergence
Chen, T., Vemuri, B. C., Rangarajan, A., & Eisenschenk, S. J. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0261-x · Cited by 42 · see citing works
Self-citationRavikumar, N., Gooya, A., Çimen, S., Frangi, A. F., & Taylor, Z. A. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.11.012 · Cited by 41 · see citing works
Adaptive hybrid attention network for hyperspectral image classification
Pande, S., & Banerjee, B. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.01.015 · Cited by 41 · see citing works
Castellani, U., & Bartoli, A. (2012) · Springer eBooks · doi:10.1007/978-1-4471-4063-4_6 · Cited by 40 · see citing works
Robust point set registration using EM-ICP with information-theoretically optimal outlier handling
Hermans, J., Smeets, D., Vandermeulen, D., & Suetens, P. (2011) · doi:10.1109/cvpr.2011.5995744 · Cited by 39 · see citing works
Wang, F., Syeda-Mahmood, T. F., Vemuri, B. C., Beymer, D., & Rangarajan, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_80 · Cited by 34 · see citing works
Self-citationNon-parametric Jensen-Shannon Divergence
Nguyen, H. V., & Vreeken, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-23525-7_11 · Cited by 33 · see citing works
Registration of Multi-View Point Sets Under the Perspective of Expectation-Maximization
Zhu, J., Guo, R., Li, Z., Zhang, J., & Pang, S. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3024096 · Cited by 32 · see citing works
Learning Traffic Patterns at Small Airports From Flight Tracks
Mahboubi, Z., & Kochenderfer, M. J. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2598064 · Cited by 31 · see citing works
Reslicing axially sampled 3D shapes using elliptic Fourier descriptors
Jeong, Y., & Radke, R. J. (2007) · Medical Image Analysis · doi:10.1016/j.media.2006.12.003 · Cited by 30 · see citing works
Automatic Construction of Parts+Geometry Models for Initializing Groupwise Registration
Zhang, P., & Cootes, T. F. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2011.2169077 · Cited by 23 · see citing works
Approximately Global Optimization for Robust Alignment of Generalized Shapes
Li, H., Shen, T., & Huang, X. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.169 · Cited by 19 · see citing works
A New Framework for Registration of Semantic Point Clouds from Stereo and RGB-D Cameras
Zhang, R., Lin, T.-Y., Lin, C. E., Parkison, S. A., Clark, W., Grizzle, J. W., Eustice, R. M., & Ghaffari, M. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561929 · Cited by 13 · see citing works
Generalized L2-Divergence and Its Application to Shape Alignment
Wang, F., Vemuri, B. C., & Syeda-Mahmood, T. F. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02498-6_19 · Cited by 14 · see citing works
Self-citationAn efficient EM-ICP algorithm for non-linear registration of large 3D point sets
Combès, B., & Prima, S. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102854 · Cited by 13 · see citing works
Arora, G., Singh, A., Nigam, A., Pandey, H. M., & Tiwari, K. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.108028 · Cited by 13 · see citing works
Hubless keypoint-based 3D deformable groupwise registration
Agier, R., Valette, S., Kéchichian, R., Fanton, L., & Prost, R. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101564 · Cited by 11 · see citing works
Free form shape registration using the barrier method
Liu, Y. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.06.005 · Cited by 10 · see citing works
A Mixture of Transformed Hidden Markov Models for Elastic Motion Estimation
Di, H., Tao, L., & Xu, G. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.111 · Cited by 9 · see citing works
Diffeomorphic Surface Registration with Atrophy Constraints
Arguillère, S., Miller, M. I., & Laurent Younes, B. (2016) · SIAM Journal on Imaging Sciences · doi:10.1137/15m104431x · Cited by 9 · see citing works
Transitive inverse-consistent image registration and evaluation
Geng, X. (2007) · doi:10.17077/etd.ni8hjlgy · Cited by 9 · see citing works
Penalizing Closest Point Sharing for Automatic Free Form Shape Registration
Liu, Y. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.207 · Cited by 8 · see citing works
Group-Wise Point-Set Registration Based on Rényi's Second Order Entropy
Giraldo, L. G. S., Hasanbelliu, E., Rao, M., & Príncipe, J. C. (2017) · 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.2017.263 · Cited by 8 · see citing works
RSC-WSRGAN super-resolution reconstruction based on improved generative adversarial network
Peng, T., & Yang, D. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03432-6 · Cited by 7 · see citing works
Wang, L., Zhou, N., Huang, H., Wang, J., Xiang, L., & Fang, Y. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3230413 · Cited by 6 · see citing works
Liang, D., Wu, Y., & Xu, Z. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3165976 · Cited by 6 · see citing works
Joint Reconstruction and Registration of a Deformable Planar Surface Observed by a 3D Sensor
Castellani, U., Gay‐Bellile, V., & Bartoli, A. (2007) · International Conference on 3-D Digital Imaging and Modeling (3DIM) · doi:10.1109/3dim.2007.31 · Cited by 6 · see citing works
Kimura, M., & Hino, H. (2021) · Entropy · doi:10.3390/e23050528 · Cited by 5 · see citing works
Robust Multi-view Registration of Point Sets with Laplacian Mixture Model
Jin, Z., Zhao, M., Jiang, X., & Dong-ming Yan, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-02444-3_41 · Cited by 4 · see citing works
Groupwise Shape Registration on Raw Edge Sequence via A Spatio-Temporal Generative Model
Di, H., Iqbal, R. N., Xu, G., & Tao, L. (2007) · 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.2007.383023 · Cited by 3 · see citing works
Unsupervised multi scale anomaly detection in streams of events
Plessis, Q., Suzuki, M., & Kitahara, T. (2016) · International Conference on Signal Processing and Communication Systems · doi:10.1109/icspcs.2016.7843324 · Cited by 2 · see citing works
Unbiased groupwise registration for shape prediction of foot scans
Zhu, J., Wang, X., Ma, S., Fan, J., Song, S., Ma, X., Ai, D., Song, H., Jiang, Y., Wang, Y., et al. (2019) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-019-01992-1 · Cited by 2 · see citing works
Stratified regularity measures with Jensen-Shannon divergence
Okada, K., Periaswamy, S., & Bi, J. (2008) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2008.4563020 · Cited by 1 · see citing works
Non-Rigid Multiple Point Set Registration Using Latent Gaussian Mixture
Huang, H., Chen, C., & Fang, Y. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897166 · Cited by 1 · see citing works
Wang, L., Xiang, L., & Fang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.12979 · Cited by 1 · see citing works
DeepTeeth: A Teeth-photo Based Human Authentication System for Mobile and Hand-held DevicesPreprint
Arora, G., Bharadwaj, R., & Tiwari, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.13217 · Cited by 1 · see citing works
Tustison, N. J., Awate, S. P., & Gee, J. C. (2008) · The Insight Journal · doi:10.54294/qw6pt8 · Cited by 1 · see citing works
Accurate Overlap Area Detection Using a Histogram and Multiple Closest Points
Liu, Y., Martin, R. R., Li, L., & Wei, B. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15907-7_13
Tustison, N. J., Awate, S. P., Altes, T. A., Gee, J. C., Cai, J., Miller, G. W., de Lange, E. E., & Mugler, J. P. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541110
Toward a Unified Framework for Point Set Registration
Li, F., Fujiwara, K., & Matsushita, Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561594
A Touchless Palm-Photo Recognition System for Mobile and Handheld Devices
Arora, G., Tiwari, U., Bhatia, A., & Tiwari, K. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-5212-6_12
- Authors
- Wang, F., Vemuri, B. C., & Rangarajan, A.
- Affiliations
- University of Florida (US)
- Venue
- 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (CVPR'06)
- Publisher
- IEEE
- Published
- 2006-07-10
- Volume
- 1
- Pages
- 1283-1288
- ISSN
- 1063-6919 (ISSN-L)
- PMC
- PMC2943644
- OpenAlex
- W2123761600
- Cited by
- 47
- References
- 10
- Citations by year
- 2025: 1 · 2024: 1 · 2022: 5 · 2021: 6 · 2020: 2 · 2019: 4 · 2017: 2 · 2016: 3 · 2015: 1 · 2012: 3
- FWCI
- 10.33
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS046812)
- Topics
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- groupwise registration, non-rigid registration, point cloud registration, cumulative distribution function, Jensen-Shannon divergence, point distribution model, 3D range data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
