Vic Patrangenaru

1.2k total citations
35 papers, 592 citations indexed

About

Vic Patrangenaru is a scholar working on Geometry and Topology, Computer Vision and Pattern Recognition and Environmental Engineering. According to data from OpenAlex, Vic Patrangenaru has authored 35 papers receiving a total of 592 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Geometry and Topology, 11 papers in Computer Vision and Pattern Recognition and 9 papers in Environmental Engineering. Recurrent topics in Vic Patrangenaru's work include Morphological variations and asymmetry (29 papers), Soil Geostatistics and Mapping (8 papers) and Statistical and numerical algorithms (6 papers). Vic Patrangenaru is often cited by papers focused on Morphological variations and asymmetry (29 papers), Soil Geostatistics and Mapping (8 papers) and Statistical and numerical algorithms (6 papers). Vic Patrangenaru collaborates with scholars based in United States, United Kingdom and Germany. Vic Patrangenaru's co-authors include Rabi Bhattacharya, Kanti V. Mardia, Martin Crane, X. Liu, F.H. Ruymgaart, Robert L. Paige, Armin Schwartzman, David Groisser, Axel Munk and Ritwik Bhattacharya and has published in prestigious journals such as Pattern Recognition, The Annals of Statistics and Journal of Multivariate Analysis.

In The Last Decade

Vic Patrangenaru

34 papers receiving 547 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Vic Patrangenaru United States 12 409 173 152 126 125 35 592
Huiling Le United Kingdom 13 375 0.9× 91 0.5× 53 0.3× 160 1.3× 58 0.5× 44 557
Alfred Kume United Kingdom 10 181 0.4× 128 0.7× 70 0.5× 64 0.5× 54 0.4× 28 338
D. Barden United Kingdom 7 262 0.6× 39 0.2× 19 0.1× 102 0.8× 27 0.2× 13 417
David Groisser United States 11 135 0.3× 24 0.1× 10 0.1× 58 0.5× 10 0.1× 27 347
Xavier Gérard Viennot France 11 48 0.1× 56 0.3× 69 0.5× 17 0.1× 15 0.1× 19 328
Hui Rao China 18 213 0.5× 47 0.3× 13 0.1× 28 0.2× 18 0.1× 65 836
Satoshi Kuriki Japan 14 36 0.1× 84 0.5× 222 1.5× 6 0.0× 26 0.2× 55 539
Jeff Calder United States 13 35 0.1× 87 0.5× 61 0.4× 106 0.8× 3 0.0× 41 374
Jean Meinguet Belgium 6 23 0.1× 25 0.1× 21 0.1× 95 0.8× 18 0.1× 13 334
Marco Perone-Pacifico Italy 6 22 0.1× 65 0.4× 62 0.4× 63 0.5× 10 0.1× 9 193

Countries citing papers authored by Vic Patrangenaru

Since Specialization
Citations

This map shows the geographic impact of Vic Patrangenaru's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vic Patrangenaru with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vic Patrangenaru more than expected).

Fields of papers citing papers by Vic Patrangenaru

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Vic Patrangenaru. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vic Patrangenaru. The network helps show where Vic Patrangenaru may publish in the future.

Co-authorship network of co-authors of Vic Patrangenaru

This figure shows the co-authorship network connecting the top 25 collaborators of Vic Patrangenaru. A scholar is included among the top collaborators of Vic Patrangenaru based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vic Patrangenaru. Vic Patrangenaru is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Lee, Hwi-Young & Vic Patrangenaru. (2024). Extrinsic kernel ridge regression classifier for Kendall’s planar shape space. Pattern Recognition. 161. 111297–111297. 1 indexed citations
2.
Lee, Hwi-Young, et al.. (2023). Test for Homogeneity of Random Objects on Manifolds with Applications to Biological Shape Analysis. Sankhya A. 85(2). 1178–1204. 1 indexed citations
3.
Patrangenaru, Vic & Yi‐Fang Deng. (2020). Extrinsic Regression and Anti-Regression on Projective Shape Manifolds. Methodology And Computing In Applied Probability. 23(2). 629–646. 2 indexed citations
4.
Paige, Robert L., et al.. (2019). A nonparametric approach to 3D shape analysis from digital camera images – II. Journal of Applied Statistics. 46(15). 2677–2699. 1 indexed citations
5.
Groisser, David, et al.. (2016). Nonparametric bootstrap of sample means of positive-definite matrices with an application to diffusion-tensor-imaging data analysis. Communications in Statistics - Simulation and Computation. 46(6). 4851–4879. 4 indexed citations
6.
Bullitt, Elizabeth, Stephan Huckemann, Ezra Miller, et al.. (2014). Tree-Oriented Analysis of Brain Artery Structure. Journal of Mathematical Imaging and Vision. 50(1-2). 126–143. 8 indexed citations
7.
Patrangenaru, Vic, et al.. (2013). Nonparametric estimation of means on Hilbert manifolds and extrinsic analysis of mean shapes of contours. Journal of Multivariate Analysis. 122. 317–333. 13 indexed citations
8.
Patrangenaru, Vic, et al.. (2013). Nonparametric two-sample tests on homogeneous Riemannian manifolds, Cholesky decompositions and Diffusion Tensor Image analysis. Journal of Multivariate Analysis. 119. 163–175. 15 indexed citations
9.
Bhattacharya, Rabi & Vic Patrangenaru. (2013). Statistics on manifolds and landmarks based image analysis: A nonparametric theory with applications. Journal of Statistical Planning and Inference. 145. 1–22. 5 indexed citations
10.
Bhattacharya, Rabi & Vic Patrangenaru. (2013). Withdrawn: Statistics on manifolds andlandmarks based image analysis: A nonparametric theory with applications. Journal of Statistical Planning and Inference. 2 indexed citations
11.
Crane, Martin & Vic Patrangenaru. (2010). Random change on a Lie group and mean glaucomatous projective shape change detection from stereo pair images. Journal of Multivariate Analysis. 102(2). 225–237. 13 indexed citations
12.
Crane, Monique F., et al.. (2009). Projective shape manifolds and coplanarity of landmark configurations. A nonparametric approach.. 14(1). 1–10. 6 indexed citations
13.
Bhattacharya, Rabi, et al.. (2009). Nonparametric inference for extrinsic means on size-and-(reflection)-shape manifolds with applications in medical imaging. Journal of Multivariate Analysis. 100(9). 1867–1882. 16 indexed citations
14.
Patrangenaru, Vic, et al.. (2009). A nonparametric approach to 3D shape analysis from digital camera images — I. Journal of Multivariate Analysis. 101(1). 11–31. 18 indexed citations
15.
Munk, Axel, Robert L. Paige, Jong‐Shi Pang, Vic Patrangenaru, & F.H. Ruymgaart. (2007). The one- and multi-sample problem for functional data with application to projective shape analysis. Journal of Multivariate Analysis. 99(5). 815–833. 16 indexed citations
16.
Patrangenaru, Vic, et al.. (2006). Geometry of Shape Spaces. 1 indexed citations
17.
Mardia, Kanti V. & Vic Patrangenaru. (2005). Directions and projective shapes. The Annals of Statistics. 33(4). 50 indexed citations
18.
Mardia, Kanti V., Vic Patrangenaru, Gordana Derado, & Vic Patrangenaru. (2003). Reconstruction of planar scenes from multiple views using affine and projective shape manifolds. 1 indexed citations
19.
Bhattacharya, Rabi & Vic Patrangenaru. (2003). Large sample theory of intrinsic and extrinsic sample means on manifolds. The Annals of Statistics. 31(1). 195 indexed citations
20.
Bhattacharya, Rabi & Vic Patrangenaru. (2002). Nonparametic estimation of location and dispersion on Riemannian manifolds. Journal of Statistical Planning and Inference. 108(1-2). 23–35. 45 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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