Jonathan Campbell

515 citations
20 papers · 256 · h-index 6

Impact in

Papers in

Jonathan Campbell

17 papers receiving 230 citations

Peers

Jonathan Campbell
Comparison fields: 5 of 72
  • Industrial and Manufacturing Engineering 113
  • Media Technology 51
  • Computer Vision and Pattern Recognition 93
  • Statistics and Probability 26
  • Artificial Intelligence 87
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Citations per field
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Citations per year

Countries citing papers authored by Jonathan Campbell

Since Specialization
Citations

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

Fields of papers citing papers by Jonathan Campbell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 1997110
2 199853
3 199937
4 200113
5
Deep Reinforcement Learning for Subpixel Neural Tracking
20189
6 20006
7 20105
8 20035
9
Clustering Player Paths.
20154
10 19984
11 20003
12 19962
13
A business guide to European Community legislation
19951
14 20231
15 20211
16 20001
17 19991
18 20170
19 20240
20
Algorithms and Data Structures for Games Programming
20090

About Jonathan Campbell

Jonathan Campbell is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Industrial and Manufacturing Engineering and Signal Processing, having authored 20 papers that have together received 256 indexed citations. Recurring topics across this work include Artificial Intelligence in Games (4 papers), Neural Networks and Applications (4 papers), Industrial Vision Systems and Defect Detection (3 papers), Optical measurement and interference techniques (2 papers), Reinforcement Learning in Robotics (2 papers), Textile materials and evaluations (2 papers), EU Law and Policy Analysis (1 paper) and Online and Blended Learning (1 paper). The work is most often cited by research in Industrial and Manufacturing Engineering (113 citations), Media Technology (51 citations), Computer Vision and Pattern Recognition (93 citations), Statistics and Probability (26 citations) and Artificial Intelligence (87 citations). Jonathan Campbell has collaborated with scholars based in United Kingdom, Canada and Ireland. Frequent co-authors include Fionn Murtagh, Chris Fraley, Adrian E. Raftery, David R. Stanford, Münevver Köküer, Guojun Zheng, Alexandre Aussem, Clark Verbrugge, J.A. Webb and Anil A. Bharath. Their work appears in journals such as Neurocomputing, Artificial Intelligence Review, International Journal of Imaging Systems and Technology, Pattern Recognition Letters and IEEE Transactions on Education.

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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