T.J. Stonham

955 citations
52 papers · 579 indexed · h-index 12

Impact in

Papers in

T.J. Stonham

48 papers receiving 515 citations

Peers

T.J. Stonham
Comparison fields: 5 of 64
  • Computer Vision and Pattern Recognition 248
  • Artificial Intelligence 240
  • Energy Engineering and Power Technology 17
  • Media Technology 33
  • Electrical and Electronic Engineering 181
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Heng Lu China
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Citations per field
00.5×2.7×
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Citations per year

Countries citing papers authored by T.J. Stonham

Since Specialization
Citations

This map shows the geographic impact of T.J. Stonham'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 T.J. Stonham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T.J. Stonham more than expected).

Fields of papers citing papers by T.J. Stonham

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by T.J. Stonham. 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 T.J. Stonham. The network helps show where T.J. Stonham may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown
#Work
1 20023
2 20024
3 20020
4 20006
5 1999157
6 19996
7 19951
8 19951
9 19954
10
Adaptive associative reward-penalty algorithms for sigma-pi networks
19944
11 19924
12 19921
13 199212
14
A fully integrated hand-printed character recognition system using artificial neural networks
19911
15
Universal architectures for logical neural nets
19911
16 19913
17 19911
18 198911
19 197512
20 19739

About T.J. Stonham

T.J. Stonham is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Human-Computer Interaction, Industrial and Manufacturing Engineering and Media Technology, having authored 52 papers that have together received 579 indexed citations. Recurring topics across this work include Neural Networks and Applications (26 papers), Image Retrieval and Classification Techniques (15 papers), Medical Image Segmentation Techniques (10 papers), Fuzzy Logic and Control Systems (9 papers), Advanced Memory and Neural Computing (5 papers), Advanced Image and Video Retrieval Techniques (5 papers), Industrial Vision Systems and Defect Detection (5 papers) and Remote-Sensing Image Classification (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (248 citations), Artificial Intelligence (240 citations), Energy Engineering and Power Technology (17 citations), Media Technology (33 citations) and Electrical and Electronic Engineering (181 citations). T.J. Stonham has collaborated with scholars based in United Kingdom, Brunei and China. Frequent co-authors include Igor Aleksander, Yong Song, Che-Wei Chou, Andrew J. Schofield, P. Mehta, Michael Shaw, George Tambouratzis, W.D. Penny, Chun Li and Shengwei Zhang. Their work appears in journals such as Electronics Letters, Neural Networks, Pattern Recognition, Analytical Chemistry and Automation in Construction.

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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