Ruwan Tennakoon
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Radiology, Nuclear Medicine and Imaging
- Aerospace Engineering
- Ophthalmology top 5%
- Co-authors
- Alireza Bab‐HadiasharReza HoseinnezhadAmirali Khodadadian GostarDavid SuterLuigi ChisciGiorgio BattistelliZhenwei CaoPallab Roy
- Topics
- Advanced Vision and Imaging (11 papers)3D Surveying and Cultural Heritage (7 papers)Remote Sensing and LiDAR Applications (6 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceScientific ReportsIEEE Transactions on Image Processing
- Partner nations
- AustraliaDenmarkUnited Kingdom
In The Last Decade
Ruwan Tennakoon
47 papers receiving 472 citations
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 232
- Artificial Intelligence 152
- Radiology, Nuclear Medicine and Imaging 104
- Aerospace Engineering 84
- Ophthalmology 62
Countries citing papers authored by Ruwan Tennakoon
This map shows the geographic impact of Ruwan Tennakoon'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 Ruwan Tennakoon with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ruwan Tennakoon more than expected).
Fields of papers citing papers by Ruwan Tennakoon
This network shows the impact of papers produced by Ruwan Tennakoon. 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 Ruwan Tennakoon. The network helps show where Ruwan Tennakoon may publish in the future.
Co-authorship network of co-authors of Ruwan Tennakoon
This figure shows the co-authorship network connecting the top 25 collaborators of Ruwan Tennakoon. A scholar is included among the top collaborators of Ruwan Tennakoon 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 Ruwan Tennakoon. Ruwan Tennakoon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 0 | |
| 3 | 3 | |
| 4 | 0 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 1 | |
| 8 | 1 | |
| 9 | 0 | |
| 10 | 13 | |
| 11 | 11 | |
| 12 | 14 | |
| 13 | 20 | |
| 14 | 15 | |
| 15 | 3 | |
| 16 | 38 | |
| 17 | 2 | |
| 18 | 25 | |
| 19 | 3 | |
| 20 | 5 |
About Ruwan Tennakoon
Ruwan Tennakoon is a scholar working on Computer Vision and Pattern Recognition, Geology and Artificial Intelligence, having authored 51 papers that have together received 488 indexed citations. Recurring topics across this work include Advanced Vision and Imaging (11 papers), 3D Surveying and Cultural Heritage (7 papers) and Remote Sensing and LiDAR Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (232 citations), Geology (48 citations) and Ophthalmology (62 citations). Ruwan Tennakoon has collaborated with scholars based in Australia, Denmark and United Kingdom. Frequent co-authors include Alireza Bab‐Hadiashar, Reza Hoseinnezhad, Amirali Khodadadian Gostar, David Suter, Luigi Chisci, Giorgio Battistelli, Zhenwei Cao, Pallab Roy, Suman Sedai and Rahil Garnavi. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and IEEE Transactions on Image 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.