Umesh Rajashekar
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience top 10%
- Human-Computer Interaction top 5%
- Media Technology top 5%
- Sensory Systems top 10%
- Co-authors
- Alan C. BovikLawrence K. CormackIan van der LindeJun MarutaJamshid GhajarLisa SpielmanZhou WangEero P. Simoncelli
- Topics
- Visual perception and processing mechanisms (11 papers)Visual Attention and Saliency Detection (10 papers)Gaze Tracking and Assistive Technology (5 papers)
- Partner nations
- United StatesUnited KingdomCanada
In The Last Decade
Umesh Rajashekar
21 papers receiving 472 citations
Peers
Comparison fields: 5 of 66
- Computer Vision and Pattern Recognition 337
- Cognitive Neuroscience 203
- Human-Computer Interaction 108
- Media Technology 63
- Sensory Systems 46
Countries citing papers authored by Umesh Rajashekar
This map shows the geographic impact of Umesh Rajashekar'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 Umesh Rajashekar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Umesh Rajashekar more than expected).
Fields of papers citing papers by Umesh Rajashekar
This network shows the impact of papers produced by Umesh Rajashekar. 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 Umesh Rajashekar. The network helps show where Umesh Rajashekar may publish in the future.
Co-authorship network of co-authors of Umesh Rajashekar
This figure shows the co-authorship network connecting the top 25 collaborators of Umesh Rajashekar. A scholar is included among the top collaborators of Umesh Rajashekar 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 Umesh Rajashekar. Umesh Rajashekar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 26 | |
| 3 | 18 | |
| 4 | 15 | |
| 5 | 9 | |
| 6 | 2 | |
| 7 | 28 | |
| 8 | 1 | |
| 9 | 63 | |
| 10 | 10 | |
| 11 | 12 | |
| 12 | 121 | |
| 13 | 1 | |
| 14 | 27 | |
| 15 | 61 | |
| 16 | 6 | |
| 17 | 32 | |
| 18 | 24 | |
| 19 | 22 | |
| 20 | 17 |
About Umesh Rajashekar
Umesh Rajashekar is a scholar working on Human-Computer Interaction, Media Technology and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 497 indexed citations. Recurring topics across this work include Visual perception and processing mechanisms (11 papers), Visual Attention and Saliency Detection (10 papers) and Gaze Tracking and Assistive Technology (5 papers). The work is most often cited by research in Human-Computer Interaction (108 citations), Computer Vision and Pattern Recognition (337 citations) and Cognitive Neuroscience (203 citations). Umesh Rajashekar has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Alan C. Bovik, Lawrence K. Cormack, Ian van der Linde, Jun Maruta, Jamshid Ghajar, Lisa Spielman, Zhou Wang, Eero P. Simoncelli, Kristin J. Heaton and Jianliang Tong. Their work appears in journals such as IEEE Transactions on Image Processing, Vision Research and Journal of Vision.
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.