James S. Painter

1.1k citations
24 papers · 880 · h-index 12

Impact in

Papers in

James S. Painter

24 papers receiving 796 citations

Peers

James S. Painter
Comparison fields: 5 of 62
  • Computer Graphics and Computer-Aided Design 735
  • Computer Vision and Pattern Recognition 648
  • Computational Mechanics 353
  • Geology 24
  • Signal Processing 39
Replace Arthur Appel with:
Arthur Appel United States
Magnus Strengert Germany
Martin E. Newell United States
Darwyn Peachey Canada
Jesse D. Hall United States
Randi J. Rost United States
Greg Coombe United States
Laurent Moll France
José A. Iglesias-Guitián Italy
Stefan Popov United States
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Citations per field
00.5×1.5×1.9×
Arthur Appel · 1×
Citations per year

Countries citing papers authored by James S. Painter

Since Specialization
Citations

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

Fields of papers citing papers by James S. Painter

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James S. Painter. 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 James S. Painter. The network helps show where James S. Painter may publish in the future.

Co-authors

The 18 scholars most cited alongside James S. Painter, 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 James S. Painter Line = papers co-authored together James S. Painter links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1994219
2 1989115
3 1993100
4 199386
5 200169
6 198964
7 199255
8 199131
9 198830
10 199224
11 200816
12 199313
13 198612
14 19909
15 19917
16 19977
17
The ACL Message Passing Library
19955
18 19965
19 20024
20 20023

About James S. Painter

James S. Painter is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Computational Mechanics, Geology and Geochemistry and Petrology, having authored 24 papers that have together received 880 indexed citations. Recurring topics across this work include Computer Graphics and Visualization Techniques (17 papers), 3D Shape Modeling and Analysis (8 papers), Advanced Vision and Imaging (6 papers), Advanced Image and Video Retrieval Techniques (3 papers), Advanced Image Processing Techniques (3 papers), Advanced Numerical Analysis Techniques (3 papers), Data Visualization and Analytics (3 papers) and Robotics and Sensor-Based Localization (3 papers). The work is most often cited by research in Computer Graphics and Computer-Aided Design (735 citations), Computer Vision and Pattern Recognition (648 citations), Computational Mechanics (353 citations), Geology (24 citations) and Signal Processing (39 citations). James S. Painter has collaborated with scholars based in United States. Frequent co-authors include Kenneth R. Sloan, Charles D. Hansen, Michael F. Krogh, Kwan‐Liu Ma, Michael F. Cohen, Patrick S. McCormick, James Paul Ahrens, Joe Michael Kniss, Allen L. McPherson and Stephen Mann. Their work appears in journals such as IEEE Computer Graphics and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, Computers & Graphics, Parallel Computing and ACM SIGGRAPH Computer Graphics.

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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