S. D. Hordley

22 papers receiving 1.3k citations

Hit Papers

On the removal of shadows from images20062026201220192006100200300400

Peers

S. D. Hordley
Comparison fields: 5 of 60
  • Computer Vision and Pattern Recognition 1.2k
  • Atomic and Molecular Physics, and Optics 770
  • Media Technology 325
  • Social Psychology 190
  • Cognitive Neuroscience 113
Replace Arjan Gijsenij with:
Arjan Gijsenij Netherlands
Claudio Cusano Italy
Alessandro Artusi United Kingdom
Imari Sato Japan
Steven D. Hordley United Kingdom
María Vanrell Spain
Rajeev Ramanath United States
Gary W. Meyer United States
Francesco Banterle Italy
Weihua Xiong China
S. D. Hordley relative to Arjan Gijsenij Netherlands Arjan Gijsenij's profile →
Citations per field
00.5×1.5×
Arjan Gijsenij · 1×
Citations per year

Countries citing papers authored by S. D. Hordley

Since Specialization
Citations

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

Fields of papers citing papers by S. D. Hordley

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. D. Hordley

This figure shows the co-authorship network connecting the top 25 collaborators of S. D. Hordley. A scholar is included among the top collaborators of S. D. Hordley based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with S. D. Hordley. S. D. Hordley is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
On the removal of shadows from imagesbreakdown →
453
2 2
3 97
4
Chromagenic Filter Design
5
5 34
6
Combining physical and statistical evidence for computational colour constancy
4
7 5
8
Chromagenic Colour Constancy
11
9 29
10 25
11 56
12 4
13
Recovery of a chromaticity image free from shadows via illumination invariance
21
14 61
15 1
16 404
17 75
18 6
19 22
20
Recovering Device Sensitiviies with Quadratic Programming
52

About S. D. Hordley

S. D. Hordley is a scholar working on Computer Vision and Pattern Recognition, Atomic and Molecular Physics, and Optics and Cognitive Neuroscience, having authored 22 papers that have together received 1.4k indexed citations. Recurring topics across this work include Color Science and Applications (19 papers), Image Enhancement Techniques (18 papers) and Visual perception and processing mechanisms (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.2k citations), Media Technology (325 citations) and Computer Graphics and Computer-Aided Design (88 citations). S. D. Hordley has collaborated with scholars based in United Kingdom, United States and Canada. Frequent co-authors include Graham D. Finlayson, Paul M. Hubel, Mark S. Drew, Cheng Lu, Ingeborg Tastl, Peter Morovič, Gerald Schaefer, J.A. Marchant and C.M. Onyango. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

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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