Siddharth Manay
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics
- Artificial Intelligence
- Aerospace Engineering
- Radiology, Nuclear Medicine and Imaging
- Topics
- Image Retrieval and Classification Techniques (3 papers)Medical Image Segmentation Techniques (3 papers)Generative Adversarial Networks and Image Synthesis (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionComputer Graphics and Computer-Aided DesignComputational Mechanics
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image ProcessingSMARTech Repository (Georgia Institute of Technology)
- Partner nations
- United StatesGermany
In The Last Decade
Siddharth Manay
5 papers receiving 191 citations
Peers
Comparison fields: 5 of 47
- Computer Vision and Pattern Recognition 154
- Computational Mechanics 40
- Artificial Intelligence 21
- Aerospace Engineering 17
- Radiology, Nuclear Medicine and Imaging 14
Countries citing papers authored by Siddharth Manay
This map shows the geographic impact of Siddharth Manay'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 Siddharth Manay with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Siddharth Manay more than expected).
Fields of papers citing papers by Siddharth Manay
This network shows the impact of papers produced by Siddharth Manay. 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 Siddharth Manay. The network helps show where Siddharth Manay may publish in the future.
Co-authorship network of co-authors of Siddharth Manay
This figure shows the co-authorship network connecting the top 25 collaborators of Siddharth Manay. A scholar is included among the top collaborators of Siddharth Manay 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 Siddharth Manay. Siddharth Manay 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 | 161 | |
| 3 | 4 | |
| 4 | 2 | |
| 5 | 30 | |
| 6 | 2 |
About Siddharth Manay
Siddharth Manay is a scholar working on Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition and Geometry and Topology, having authored 6 papers that have together received 199 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (3 papers), Medical Image Segmentation Techniques (3 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (154 citations), Computer Graphics and Computer-Aided Design (9 citations) and Computational Mechanics (40 citations). Siddharth Manay has collaborated with scholars based in United States and Germany. Frequent co-authors include Anthony Yezzi, Byung‐Woo Hong, Stefano Soatto, Daniel Cremers and David W. Paglieroni. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and SMARTech Repository (Georgia Institute of Technology).
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.