Yvan G. Leclerc

19 papers receiving 875 citations

Peers

Yvan G. Leclerc
Comparison fields: 5 of 103
  • Computer Vision and Pattern Recognition 637
  • Cognitive Neuroscience 174
  • Aerospace Engineering 120
  • Computational Mechanics 109
  • Computer Graphics and Computer-Aided Design 102
Replace Frédéric Fol Leymarie with:
Frédéric Fol Leymarie United Kingdom
H. Harlyn Baker United States
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Yvan G. Leclerc relative to Frédéric Fol Leymarie United Kingdom Frédéric Fol Leymarie's profile →
Citations per field
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Citations per year

Countries citing papers authored by Yvan G. Leclerc

Since Specialization
Citations

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

Fields of papers citing papers by Yvan G. Leclerc

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yvan G. Leclerc. 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 Yvan G. Leclerc. The network helps show where Yvan G. Leclerc may publish in the future.

Co-authorship network of co-authors of Yvan G. Leclerc

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

All Works

19 of 19 papers shown
#WorkIndexed citations
1 7
2 4
3 25
4 5
5
Using RADIUS Site Models without the RCDE
1
6 3
7 98
8 131
9
The local structure of image intensity discontinuities
6
10 110
11 87
12 346
13
Image and boundary segmentation via minimal-length encoding on the connection machine
6
14 5
15 76
16 17
17 3
18 39
19
Continuous relaxation and local maxima selection: conditions for equivalence
5

About Yvan G. Leclerc

Yvan G. Leclerc is a scholar working on Computer Graphics and Computer-Aided Design, Geochemistry and Petrology and Geology, having authored 19 papers that have together received 974 indexed citations. Recurring topics across this work include Medical Image Segmentation Techniques (5 papers), Image and Signal Denoising Methods (3 papers) and Computer Graphics and Visualization Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (637 citations), Computer Graphics and Computer-Aided Design (102 citations) and Geology (57 citations). Yvan G. Leclerc has collaborated with scholars based in United States, Canada and Switzerland. Frequent co-authors include Pascal Fua, Steven W. Zucker, Martin A. Fischler, Patrick Cavanagh, Martin Reddy, Lee Iverson, Anthony B. Hodsman, Sarah Prichard, Michael Kaye and Q.-T. Luong. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Kidney International and Journal of Experimental Psychology Human Perception & Performance.

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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