Michaël Aupetit

1.7k citations
54 papers · 999 · h-index 15

Impact in

Papers in

Michaël Aupetit

49 papers receiving 950 citations

Peers

Michaël Aupetit
Comparison fields: 5 of 120
  • Computer Vision and Pattern Recognition 528
  • Artificial Intelligence 385
  • Computer Graphics and Computer-Aided Design 41
  • Biophysics 63
  • Experimental and Cognitive Psychology 136
Replace J.F. Tasič with:
J.F. Tasič Slovenia
Carlos Eduardo Thomaz Brazil
Yannis Panagakis United Kingdom
Angus G. Forbes United States
Oran Lang Israel
Lin Yuan China
M. Fatih Demirci Türkiye
Gertjan J. Burghouts Netherlands
Zhuhong Shao China
L. Zhao United States
Michaël Aupetit relative to J.F. Tasič Slovenia J.F. Tasič's profile →
Citations per field
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J.F. Tasič · 1×
Citations per year

Countries citing papers authored by Michaël Aupetit

Since Specialization
Citations

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

Fields of papers citing papers by Michaël Aupetit

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Michaël Aupetit, 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 Michaël Aupetit Line = papers co-authored together Michaël Aupetit links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020183
2 2018151
3 200693
4 201075
5 201963
6 201559
7 201643
8
Recent Advances in Nonlinear Dimensionality Reduction, Manifold and Topological Learning
201039
9 200833
10 201930
11 201723
12 200423
13 201419
14 200916
15
Learning Topology with the Generative Gaussian Graph and the EM Algorithm
200515
16 200813
17 201911
18 201911
19 20239
20
Interactive monitoring of critical situational information on social media
20177

About Michaël Aupetit

Michaël Aupetit is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Computational Theory and Mathematics and General Health Professions, having authored 54 papers that have together received 999 indexed citations. Recurring topics across this work include Data Visualization and Analytics (14 papers), Advanced Clustering Algorithms Research (9 papers), Image Retrieval and Classification Techniques (8 papers), Face and Expression Recognition (7 papers), Neural Networks and Applications (7 papers), Data Management and Algorithms (5 papers), Topological and Geometric Data Analysis (5 papers) and Mobile Health and mHealth Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (528 citations), Artificial Intelligence (385 citations), Computer Graphics and Computer-Aided Design (41 citations), Biophysics (63 citations) and Experimental and Cognitive Psychology (136 citations). Michaël Aupetit has collaborated with scholars based in Qatar, France and United States. Frequent co-authors include Luís Gustavo Nonato, Michael Sedlmair, Sylvain Lespinats, Luis Fernández-Luque, Shahrad Taheri, Raghvendra Mall, João Palotti, Ignacio Perez-Pozuelo, Yu Guan and Juan M. García‐Gómez. Their work appears in journals such as Neurocomputing, IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, Neural Networks and npj Digital Medicine.

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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