Vincent Duval

1.5k citations
25 papers · 744 · h-index 11

Impact in

Papers in

Vincent Duval

24 papers receiving 713 citations

Peers

Vincent Duval
Comparison fields: 5 of 62
  • Computer Vision and Pattern Recognition 371
  • Computational Mechanics 371
  • Media Technology 152
  • Mathematical Physics 148
  • Computational Mathematics 6
Replace Chengda Yang with:
Chengda Yang United States
Stamatios Lefkimmiatis Switzerland
Jinjun Xu United States
Tanja Teuber Germany
Greg Ongie United States
Ernie Esser Canada
M.R. Banham United States
Xiao-Guang Lv China
Triet Le United States
Kanghui Guo United States
Vincent Duval relative to Chengda Yang United States Chengda Yang's profile →
Citations per field
00.5×1.5×2.4×
Chengda Yang · 1×
Citations per year

Countries citing papers authored by Vincent Duval

Since Specialization
Citations

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

Fields of papers citing papers by Vincent Duval

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 20 scholars most cited alongside Vincent Duval, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Vincent Duval Line = papers co-authored together Vincent Duval links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011175
2 2014134
3 201165
4 201859
5 200955
6 201947
7 201643
8 201033
9 201729
10 201625
11 201720
12 20179
13 20179
14 20187
15 20197
16 20175
17 20225
18 20205
19 20243
20 20232

About Vincent Duval

Vincent Duval is a scholar working on Computational Mechanics, Mathematical Physics, Computer Vision and Pattern Recognition, Biomedical Engineering and Radiology, Nuclear Medicine and Imaging, having authored 25 papers that have together received 744 indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (15 papers), Numerical methods in inverse problems (10 papers), Image and Signal Denoising Methods (7 papers), Microwave Imaging and Scattering Analysis (6 papers), Medical Image Segmentation Techniques (5 papers), Medical Imaging Techniques and Applications (4 papers), Advanced Image Processing Techniques (3 papers) and Geometric Analysis and Curvature Flows (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (371 citations), Computational Mechanics (371 citations), Media Technology (152 citations), Mathematical Physics (148 citations) and Computational Mathematics (6 citations). Vincent Duval has collaborated with scholars based in France, United Kingdom and Canada. Frequent co-authors include Gabriel Peyré, Joseph Salmon, Charles‐Alban Deledalle, Jean–François Aujol, Yann Gousseau, Emmanuel Soubies, Antonin Chambolle, Luminita A. Vese, Clarice Poon and Yohann de Castro. Their work appears in journals such as Inverse Problems, Journal of Mathematical Imaging and Vision, SIAM Journal on Imaging Sciences, Journal of Fourier Analysis and Applications and European Journal of Applied Mathematics.

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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