Jean Séqueira

1.2k citations
23 papers · 355 indexed · h-index 8

Impact in

Papers in

    • Medical Image Segmentation Techniques 5
    • Face recognition and analysis 4
    • Advanced Vision and Imaging 4
    • Advanced Image and Video Retrieval Techniques 3
    • Face and Expression Recognition 2
    • Biometric Identification and Security 2

Jean Séqueira

22 papers receiving 342 citations

Peers

Jean Séqueira
Comparison fields: 5 of 86
  • Media Technology 74
  • Computer Vision and Pattern Recognition 136
  • Radiology, Nuclear Medicine and Imaging 149
  • Neurology 19
  • Genetics 20
Replace Xinhua Cao with:
Xinhua Cao United States
Guangzhe Zhao China
Jie Wei United States
Muhammad Nadeem Pakistan
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Citations per field
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Citations per year

Countries citing papers authored by Jean Séqueira

Since Specialization
Citations

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

Fields of papers citing papers by Jean Séqueira

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013145
2 202142
3 202034
4 200630
5 201917
6 200811
7 199310
8 20169
9 20077
10 20187
11 20106
12 20206
13 20176
14 20205
15 20195
16 20084
17 20104
18
Geometrical modeling of abdominal aortic aneurysms
19973
19
Poster: Classification of cell nuclei using shape and texture indexes
20081
20 20111

About Jean Séqueira

Jean Séqueira is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Computational Mechanics, Media Technology and Surgery, having authored 23 papers that have together received 355 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Face recognition and analysis (4 papers), Advanced Vision and Imaging (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Cell Image Analysis Techniques (2 papers), Face and Expression Recognition (2 papers), Biometric Identification and Security (2 papers) and Advanced Image Fusion Techniques (2 papers). The work is most often cited by research in Media Technology (74 citations), Computer Vision and Pattern Recognition (136 citations), Radiology, Nuclear Medicine and Imaging (149 citations), Neurology (19 citations) and Genetics (20 citations). Jean Séqueira has collaborated with scholars based in France, Algeria and China. Frequent co-authors include Jean‐Luc Mari, Bernard Fertil, Guillaume Thibault, Claire Navarro, Nicolas Lévy, Sandrine Pereira, Sébastien Mavromatis, Renyi Liu, Feng Zhang and Zhenhong Du. Their work appears in journals such as International Journal of Digital Earth, Computerized Medical Imaging and Graphics, IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing and EURASIP Journal on Advances in Signal Processing.

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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