John G. Keller

971 citations
7 papers · 58 · h-index 4

Impact in

Papers in

John G. Keller

5 papers receiving 55 citations

Peers

John G. Keller
Comparison fields: 5 of 32
  • Computer Vision and Pattern Recognition 27
  • Aerospace Engineering 31
  • Geology 3
  • Control and Systems Engineering 10
  • Computer Graphics and Computer-Aided Design 1
Replace Thorsten Linder with:
Thorsten Linder Germany
Olivier Koch United States
Ilze Andersone Latvia
João Mendes Portugal
Karol Jędrasiak Poland
Keerthana Gopalakrishnan United States
Gautam Salhotra United States
Xiaoshuang Ma China
P. Torr United Kingdom
Kenji Okuma Canada
John G. Keller relative to Thorsten Linder Germany Thorsten Linder's profile →
Citations per field
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Thorsten Linder · 1×
Citations per year

Countries citing papers authored by John G. Keller

Since Specialization
Citations

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

Fields of papers citing papers by John G. Keller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 13 scholars most cited alongside John G. Keller, 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 John G. Keller Line = papers co-authored together John G. Keller links everyone, so they are left out of the graph.

All Works

7 of 7 papers shown

About John G. Keller

John G. Keller is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering, Small Animals, Social Psychology and Cardiology and Cardiovascular Medicine, having authored 7 papers that have together received 58 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (2 papers), Robotic Path Planning Algorithms (2 papers), Color perception and design (1 paper), Aerospace and Aviation Technology (1 paper), Video Coding and Compression Technologies (1 paper), Advanced Vision and Imaging (1 paper), Remote Sensing and LiDAR Applications (1 paper) and Heart Rate Variability and Autonomic Control (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (27 citations), Aerospace Engineering (31 citations), Geology (3 citations), Control and Systems Engineering (10 citations) and Computer Graphics and Computer-Aided Design (1 citation). John G. Keller has collaborated with scholars based in United States, Portugal and Australia. Frequent co-authors include Sebastian Scherer, Fan Yang, Marion deV. Cotten, Neil C. Moran, Phyllis E. Stopp, Rita Cunha, A. Pascoal, Matthew Kabrisky, Mark E. Oxley and Steven K. Rogers. Their work appears in journals such as IEEE Robotics and Automation Letters, Journal of Pharmacology and Experimental Therapeutics, Pattern Analysis and Applications and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.

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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