J. Shah

511 total citations
18 papers, 295 citations indexed

About

J. Shah is a scholar working on Computer Vision and Pattern Recognition, Computational Mechanics and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, J. Shah has authored 18 papers receiving a total of 295 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 4 papers in Computational Mechanics and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in J. Shah's work include Medical Image Segmentation Techniques (11 papers), 3D Shape Modeling and Analysis (4 papers) and Image and Object Detection Techniques (3 papers). J. Shah is often cited by papers focused on Medical Image Segmentation Techniques (11 papers), 3D Shape Modeling and Analysis (4 papers) and Image and Object Detection Techniques (3 papers). J. Shah collaborates with scholars based in United States and Pakistan. J. Shah's co-authors include Homer Pien, Sibel Tarı, John M. Gauch, Zahir Tari, David N. Kennedy, Andrew J. Worth, Rami Mangoubi, M. Desai, W.C. Karl and R.J. Seitz and has published in prestigious journals such as NeuroImage, IEEE Transactions on Image Processing and IEEE Transactions on Medical Imaging.

In The Last Decade

J. Shah

15 papers receiving 280 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Shah United States 9 229 68 34 34 26 18 295
Georges Koepfler France 5 213 0.9× 43 0.6× 51 1.5× 20 0.6× 22 0.8× 11 270
Ginmo Chung United States 4 175 0.8× 76 1.1× 34 1.0× 17 0.5× 24 0.9× 4 217
M.J. Fadili France 5 243 1.1× 107 1.6× 43 1.3× 58 1.7× 8 0.3× 5 421
Luca Calatroni France 10 144 0.6× 97 1.4× 40 1.2× 21 0.6× 46 1.8× 34 292
François Malgouyres France 11 406 1.8× 171 2.5× 117 3.4× 23 0.7× 30 1.2× 34 472
Bei Tang United States 4 238 1.0× 91 1.3× 59 1.7× 25 0.7× 11 0.4× 9 334
Françoise Dibos France 6 205 0.9× 45 0.7× 73 2.1× 37 1.1× 16 0.6× 12 258
Miyoun Jung South Korea 10 294 1.3× 158 2.3× 99 2.9× 18 0.5× 34 1.3× 29 365
Evgeny Strekalovskiy Germany 9 164 0.7× 88 1.3× 23 0.7× 27 0.8× 33 1.3× 11 221
Jacques Froment France 8 216 0.9× 63 0.9× 92 2.7× 34 1.0× 8 0.3× 17 276

Countries citing papers authored by J. Shah

Since Specialization
Citations

This map shows the geographic impact of J. Shah'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 J. Shah with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Shah more than expected).

Fields of papers citing papers by J. Shah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Shah. 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 J. Shah. The network helps show where J. Shah may publish in the future.

Co-authorship network of co-authors of J. Shah

This figure shows the co-authorship network connecting the top 25 collaborators of J. Shah. A scholar is included among the top collaborators of J. Shah 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 J. Shah. J. Shah is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Shah, J. & Arthi Jayaraman. (2025). Coarse-grained molecular dynamics simulations of mixtures of polysulfamides. RSC Applied Polymers. 3(2). 453–468.
2.
Karl, W.C., et al.. (2006). Shape and appearance modelling with feature distributions for image segmentation. 1128–1131. 1 indexed citations
3.
4.
Desai, M., David N. Kennedy, Rami Mangoubi, et al.. (2003). Diffusion tensor model based smoothing. 705–708. 1 indexed citations
5.
Shah, J.. (2003). Segmentation by nonlinear diffusion. II. 644–647. 14 indexed citations
6.
Desai, M., Rami Mangoubi, J. Shah, et al.. (2002). Functional MRI activity characterization using response time shift estimates from curve evolution. IEEE Transactions on Medical Imaging. 21(11). 1402–1412. 20 indexed citations
7.
8.
Shah, J.. (2002). Segmentation as a Riemannian drum problem. 3. 766–769.
9.
10.
Shah, J.. (2002). Curve evolution and segmentation functionals: application to color images. 1. 461–464. 19 indexed citations
11.
Gauch, John M., Homer Pien, & J. Shah. (2002). Hybrid deformable models for three-dimensional biomedical image segmentation. 4. 1935–1939. 1 indexed citations
12.
Tarı, Sibel & J. Shah. (2002). Local symmetries of shapes in arbitrary dimension. 1123–1128. 19 indexed citations
13.
Shah, J.. (2002). Minimax entropy and learning by diffusion. 92–97. 3 indexed citations
14.
Shah, J.. (2002). Segmentation by nonlinear diffusion. 202–207. 18 indexed citations
15.
Weder, B., Ferdinand Binkofski, Giovanni Buccino, et al.. (2001). Temporal evolution of cerebral activation in tactile object discrimination. NeuroImage. 13(6). 1260–1260. 1 indexed citations
16.
Shah, J., Homer Pien, & John M. Gauch. (1996). Recovery of surfaces with discontinuities by fusing shading and range data within a variational framework. IEEE Transactions on Image Processing. 5(8). 1243–1251. 23 indexed citations
17.
Tari, Zahir, J. Shah, & Homer Pien. (1996). A computationally efficient shape analysis via level sets. OpenMETU (Middle East Technical University). 234–243. 16 indexed citations
18.
Shah, J.. (1996). A common framework for curve evolution, segmentation and anisotropic diffusion. 136–142. 143 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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