Maitreya Suin
- Computer Vision and Pattern Recognition top 5%
- Media Technology top 5%
- Biomedical Engineering
- Artificial Intelligence
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- A. N. RajagopalanKuldeep PurohitSoumyadipta AcharyaRama ChellappaChun Pong LauVishal M. PatelNathan ShnidmanKartik Narayan
- Topics
- Advanced Image Processing Techniques (5 papers)Face recognition and analysis (3 papers)Image and Signal Denoising Methods (2 papers)
- Journals
- Scientific ReportsIEEE Transactions on Circuits and Systems for Video TechnologyIEEE Journal of Selected Topics in Signal Processing
- Partner nations
- IndiaUnited StatesHong Kong
In The Last Decade
Maitreya Suin
9 papers receiving 286 citations
Peers
Comparison fields: 5 of 39
- Computer Vision and Pattern Recognition 275
- Media Technology 137
- Biomedical Engineering 11
- Artificial Intelligence 9
- Radiology, Nuclear Medicine and Imaging 7
Countries citing papers authored by Maitreya Suin
This map shows the geographic impact of Maitreya Suin'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 Maitreya Suin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maitreya Suin more than expected).
Fields of papers citing papers by Maitreya Suin
This network shows the impact of papers produced by Maitreya Suin. 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 Maitreya Suin. The network helps show where Maitreya Suin may publish in the future.
Co-authorship network of co-authors of Maitreya Suin
This figure shows the co-authorship network connecting the top 25 collaborators of Maitreya Suin. A scholar is included among the top collaborators of Maitreya Suin 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 Maitreya Suin. Maitreya Suin 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 | 6 | |
| 3 | 2 | |
| 4 | 12 | |
| 5 | 1 | |
| 6 | 21 | |
| 7 | 19 | |
| 8 | 199 | |
| 9 | 23 | |
| 10 | 12 |
About Maitreya Suin
Maitreya Suin is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Public Health, Environmental and Occupational Health, having authored 10 papers that have together received 295 indexed citations. Recurring topics across this work include Advanced Image Processing Techniques (5 papers), Face recognition and analysis (3 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Media Technology (137 citations), Computer Vision and Pattern Recognition (275 citations) and Acoustics and Ultrasonics (6 citations). Maitreya Suin has collaborated with scholars based in India, United States and Hong Kong. Frequent co-authors include A. N. Rajagopalan, Kuldeep Purohit, Soumyadipta Acharya, Rama Chellappa, Chun Pong Lau, Vishal M. Patel, Nathan Shnidman, Kartik Narayan, Jennifer Xu and Joshua Gleason. Their work appears in journals such as Scientific Reports, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Journal of Selected Topics in Signal Processing.
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.