Clay D. Spence

2.3k total citations
36 papers, 1.4k citations indexed

About

Clay D. Spence is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Clay D. Spence has authored 36 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 15 papers in Artificial Intelligence and 7 papers in Cognitive Neuroscience. Recurrent topics in Clay D. Spence's work include AI in cancer detection (9 papers), Image Retrieval and Classification Techniques (8 papers) and Image and Signal Denoising Methods (7 papers). Clay D. Spence is often cited by papers focused on AI in cancer detection (9 papers), Image Retrieval and Classification Techniques (8 papers) and Image and Signal Denoising Methods (7 papers). Clay D. Spence collaborates with scholars based in United States. Clay D. Spence's co-authors include Lucas C. Parra, Paul Sajda, Adam D. Gerson, John C. Pearson, Andreas Ziehe, Klaus‐Robert Müller, Bert de Vries, Peter Šajda, Jack Gelfand and W. E. Sullivan and has published in prestigious journals such as NeuroImage, IEEE Transactions on Image Processing and IEEE Transactions on Medical Imaging.

In The Last Decade

Clay D. Spence

35 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Clay D. Spence United States 13 753 586 329 264 164 36 1.4k
Kenneth E. Hild United States 20 607 0.8× 553 0.9× 173 0.5× 347 1.3× 106 0.6× 58 1.2k
Sergio Cruces Spain 16 520 0.7× 162 0.3× 208 0.6× 148 0.6× 161 1.0× 45 861
Ricardo Vigário Finland 17 1.3k 1.7× 1.3k 2.1× 93 0.3× 433 1.6× 271 1.7× 53 2.1k
Laurent Albera France 24 1.0k 1.4× 716 1.2× 200 0.6× 131 0.5× 141 0.9× 74 1.8k
Xi-Lin Li United States 18 796 1.1× 386 0.7× 199 0.6× 163 0.6× 249 1.5× 50 1.1k
F.H.Y. Chan Hong Kong 17 331 0.4× 276 0.5× 76 0.2× 114 0.4× 66 0.4× 89 976
A. J. Bell United States 7 474 0.6× 425 0.7× 91 0.3× 136 0.5× 124 0.8× 10 810
Jarmo Hurri Finland 11 289 0.4× 318 0.5× 42 0.1× 214 0.8× 75 0.5× 23 875
S. Shamsunder United States 13 679 0.9× 277 0.5× 123 0.4× 161 0.6× 57 0.3× 33 946
Yehoshua Y. Zeevi Israel 16 252 0.3× 174 0.3× 115 0.3× 57 0.2× 54 0.3× 116 999

Countries citing papers authored by Clay D. Spence

Since Specialization
Citations

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

Fields of papers citing papers by Clay D. Spence

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Clay D. Spence

This figure shows the co-authorship network connecting the top 25 collaborators of Clay D. Spence. A scholar is included among the top collaborators of Clay D. Spence 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 Clay D. Spence. Clay D. Spence 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
1.
Spence, Clay D., Lucas C. Parra, & Paul Sajda. (2006). Varying complexity in tree-structured image distribution models. IEEE Transactions on Image Processing. 15(2). 319–330. 4 indexed citations
2.
Parra, Lucas C., Clay D. Spence, Adam D. Gerson, & Paul Sajda. (2005). Recipes for the linear analysis of EEG. NeuroImage. 28(2). 326–341. 431 indexed citations
3.
Spence, Clay D., et al.. (2004). Automated Detection of Neovascularization in Diabetic Retinopathy. Investigative Ophthalmology & Visual Science. 45(13). 2984–2984. 1 indexed citations
4.
Parra, Lucas C., Clay D. Spence, Adam D. Gerson, & Paul Sajda. (2003). Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 11(2). 173–177. 104 indexed citations
5.
Sajda, Paul, Clay D. Spence, & Lucas C. Parra. (2003). A multi-scale probabilistic network model for detection, synthesis and compression in mammographic image analysis. Medical Image Analysis. 7(2). 187–204. 13 indexed citations
6.
Sajda, Paul, Clay D. Spence, & John C. Pearson. (2002). Learning contextual relationships in mammograms using a hierarchical pyramid neural network. IEEE Transactions on Medical Imaging. 21(3). 239–250. 45 indexed citations
7.
Spence, Clay D., Lucas C. Parra, & Paul Sajda. (2002). Hierarchical image probability (HIP) models. 3370. 320–323. 7 indexed citations
8.
Parra, Lucas C., Clay D. Spence, & Peter Šajda. (2000). Higher-Order Statistical Properties Arising from the Non-Stationarity of Natural Signals. Neural Information Processing Systems. 13. 786–792. 24 indexed citations
9.
Parra, Lucas C. & Clay D. Spence. (2000). On-line Blind Source Separation of Non-Stationary Signals. 2 indexed citations
10.
Parra, Lucas C. & Clay D. Spence. (2000). Convolutive blind separation of non-stationary sources. IEEE Transactions on Speech and Audio Processing. 8(3). 320–327. 499 indexed citations
11.
Spence, Clay D. & Lucas C. Parra. (1999). Hierarchical Image Probability (H1P) Models. Neural Information Processing Systems. 12. 848–854. 2 indexed citations
12.
Parra, Lucas C., Clay D. Spence, Paul Sajda, Andreas Ziehe, & Klaus‐Robert Müller. (1999). Unmixing Hyperspectral Data. Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft). 12. 942–948. 88 indexed citations
13.
Spence, Clay D. & Paul Sajda. (1998). Applications of Multi-Resolution Neural Networks to Mammography. Neural Information Processing Systems. 11. 938–944. 6 indexed citations
14.
Parra, Lucas C., Clay D. Spence, & Bert de Vries. (1997). Convolutive Source Separation and Signal Modeling with ML. 2 indexed citations
15.
Pearson, John C., Jack Gelfand, W. E. Sullivan, Richard M. Peterson, & Clay D. Spence. (1995). Neural network approach to sensory fusion. Ablex Publishing Corp. eBooks. 111–120. 1 indexed citations
16.
Pearson, John C., et al.. (1990). Applications of Neural Networks in Video Signal Processing. Neural Information Processing Systems. 3. 289–295. 1 indexed citations
17.
Spence, Clay D. & John C. Pearson. (1989). The Computation of Sound Source Elevation in the Barn Owl. Neural Information Processing Systems. 2. 10–17. 5 indexed citations
18.
Spence, Clay D., John C. Pearson, Jack Gelfand, Richard M. Peterson, & W. E. Sullivan. (1988). Neuronal Maps for Sensory-Motor Control in the Barn Owl. Neural Information Processing Systems. 1. 366–374. 5 indexed citations
19.
Pearson, John C., Clay D. Spence, Jack Gelfand, W. E. Sullivan, & Richard M. Peterson. (1988). Neuronal maps for sensory-motor control in the barn owl. Neural Networks. 1. 353–353. 1 indexed citations
20.
Pearson, John C., Jack Gelfand, W. E. Sullivan, Richard M. Peterson, & Clay D. Spence. (1988). Neural Network Approach To Sensory Fusion. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 931. 103–103. 24 indexed citations

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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