David Connah

742 citations
31 papers · 493 · h-index 11

Impact in

Papers in

David Connah

30 papers receiving 443 citations

Peers

David Connah
Comparison fields: 5 of 83
  • Computer Vision and Pattern Recognition 262
  • Media Technology 111
  • Atomic and Molecular Physics, and Optics 281
  • Analytical Chemistry 42
  • Industrial and Manufacturing Engineering 40
Replace Di-Yuan Tzeng with:
Di-Yuan Tzeng United States
Michael Stokes United States
Steven D. Hordley United Kingdom
Noriaki Suetake Japan
Chang‐Hwan Son South Korea
Vladimir Vezhnevets Tajikistan
Cong Phuoc Huynh Australia
Yeong‐Ho Ha South Korea
Seoung Wug Oh United States
Cosmin Grigorescu Netherlands
David Connah relative to Di-Yuan Tzeng United States Di-Yuan Tzeng's profile →
Citations per field
00.5×6.3×
Di-Yuan Tzeng · 1×
Citations per year

Countries citing papers authored by David Connah

Since Specialization
Citations

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

Fields of papers citing papers by David Connah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200495
2 200582
3 200159
4 201645
5 201424
6 201920
7 201619
8 200618
9 200518
10 201112
11 198510
12 19979
13 20069
14 20059
15 20098
16 20078
17 20168
18 20167
19 20157
20 20026

About David Connah

David Connah is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics, Media Technology, Biomedical Engineering and Artificial Intelligence, having authored 31 papers that have together received 493 indexed citations. Recurring topics across this work include Color Science and Applications (15 papers), Image Enhancement Techniques (10 papers), Image and Signal Denoising Methods (3 papers), Industrial Vision Systems and Defect Detection (3 papers), Visual perception and processing mechanisms (3 papers), Color perception and design (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (262 citations), Media Technology (111 citations), Atomic and Molecular Physics, and Optics (281 citations), Analytical Chemistry (42 citations) and Industrial and Manufacturing Engineering (40 citations). David Connah has collaborated with scholars based in United Kingdom, Finland and Australia. Frequent co-authors include Stephen Westland, Vien Cheung, Jon Yngve Hardeberg, Changjun Li, Caterina Ripamonti, Mitchell G. A. Thomson, Graham D. Finlayson, Hassan Ugail, Javier Vázquez-Corral and Kaida Xiao. Their work appears in journals such as Journal of the Optical Society of America A, Coloration Technology, IEEE Transactions on Image Processing, BMJ Open and Artificial Intelligence Review.

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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