Ruwan Tennakoon

1.0k total citations
51 papers, 488 citations indexed

About

Ruwan Tennakoon is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Ruwan Tennakoon has authored 51 papers receiving a total of 488 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Computer Vision and Pattern Recognition, 19 papers in Artificial Intelligence and 7 papers in Computational Mechanics. Recurrent topics in Ruwan Tennakoon's work include Advanced Vision and Imaging (11 papers), 3D Surveying and Cultural Heritage (7 papers) and Remote Sensing and LiDAR Applications (6 papers). Ruwan Tennakoon is often cited by papers focused on Advanced Vision and Imaging (11 papers), 3D Surveying and Cultural Heritage (7 papers) and Remote Sensing and LiDAR Applications (6 papers). Ruwan Tennakoon collaborates with scholars based in Australia, Denmark and United Kingdom. Ruwan Tennakoon's co-authors include Alireza Bab‐Hadiashar, Reza Hoseinnezhad, Amirali Khodadadian Gostar, David Suter, Giorgio Battistelli, Luigi Chisci, Zhenwei Cao, Suman Sedai, Rahil Garnavi and Pallab Roy and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and IEEE Transactions on Image Processing.

In The Last Decade

Ruwan Tennakoon

47 papers receiving 472 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ruwan Tennakoon Australia 15 232 152 104 84 62 51 488
Wei Ke China 12 454 2.0× 156 1.0× 109 1.0× 66 0.8× 62 1.0× 39 659
Zhenjie Yao China 10 127 0.5× 65 0.4× 69 0.7× 15 0.2× 41 0.7× 35 374
Misha Urooj Khan Pakistan 11 103 0.4× 49 0.3× 45 0.4× 80 1.0× 9 0.1× 30 315
Xinnan Fan China 14 342 1.5× 55 0.4× 19 0.2× 93 1.1× 6 0.1× 57 609
Cong Lin China 12 145 0.6× 44 0.3× 31 0.3× 57 0.7× 4 0.1× 74 555
Ricardo Toledo Spain 16 761 3.3× 124 0.8× 67 0.6× 304 3.6× 14 0.2× 37 867
Massimiliano Mancini Italy 11 292 1.3× 438 2.9× 105 1.0× 27 0.3× 5 0.1× 29 607
Boubakeur Boufama Canada 13 476 2.1× 103 0.7× 17 0.2× 94 1.1× 4 0.1× 59 603
Jingyu Li China 9 232 1.0× 94 0.6× 23 0.2× 46 0.5× 7 0.1× 29 362

Countries citing papers authored by Ruwan Tennakoon

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Tennakoon, Ruwan, Aref Miri Rekavandi, Mark Easton, et al.. (2025). IT-RUDA: Information Theory-Assisted Robust Unsupervised Domain Adaptation. ACM Transactions on Intelligent Systems and Technology. 16(4). 1–17. 1 indexed citations
2.
Mahapatra, Dwarikanath, Ruwan Tennakoon, Yasmeen George, et al.. (2024). ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification. Medical Image Analysis. 97. 103261–103261. 3 indexed citations
3.
Tennakoon, Ruwan, et al.. (2024). Single Domain Generalization via Normalised Cross-correlation Based Convolutions. 1741–1750. 3 indexed citations
4.
Gostar, Amirali Khodadadian, et al.. (2023). Temporal Multiple Rotation Averaging on a Distributed Dynamic Network. IEEE Transactions on Signal and Information Processing over Networks. 9. 669–678.
5.
Gostar, Amirali Khodadadian, Ruwan Tennakoon, Alireza Bab‐Hadiashar, et al.. (2022). Distributed Multi-Sensor Control for Multi-Target Tracking. 231–239. 1 indexed citations
6.
Suter, David, et al.. (2022). Maximum Consensus by Weighted Influences of Monotone Boolean Functions. Australasian Journal of Paramedicine. 8954–8962. 2 indexed citations
7.
Tennakoon, Ruwan, et al.. (2022). Semantic Guided Long Range Stereo Depth Estimation for Safer Autonomous Vehicle Applications. IEEE Transactions on Intelligent Transportation Systems. 23(10). 18916–18926. 13 indexed citations
8.
Tennakoon, Ruwan, et al.. (2022). Robust pooling through the data mode. Intelligent Systems with Applications. 17. 200162–200162. 1 indexed citations
9.
Tennakoon, Ruwan, et al.. (2021). Deep Learning-Based Incorporation of Planar Constraints for Robust Stereo Depth Estimation in Autonomous Vehicle Applications. IEEE Transactions on Intelligent Transportation Systems. 23(7). 6654–6665. 20 indexed citations
10.
Tennakoon, Ruwan, et al.. (2021). Incidental detection of prostate cancer with computed tomography scans. Scientific Reports. 11(1). 7956–7956. 14 indexed citations
11.
Ciesielski, Vic, et al.. (2021). Annotation of Struck-out Text in Handwritten Documents. 1–7. 1 indexed citations
12.
Mayo, S. C., et al.. (2021). Automatic segmentation for synchrotron-based imaging of porous bread dough using deep learning approach. Journal of Synchrotron Radiation. 28(2). 566–575. 11 indexed citations
13.
Gostar, Amirali Khodadadian, et al.. (2020). On-Line Visual Tracking with Occlusion Handling. Sensors. 20(3). 929–929. 17 indexed citations
14.
Tennakoon, Ruwan, Gerda Bortsova, Amirali Khodadadian Gostar, et al.. (2019). Classification of Volumetric Images Using Multi-Instance Learning and Extreme Value Theorem. IEEE Transactions on Medical Imaging. 39(4). 854–865. 15 indexed citations
15.
Tennakoon, Ruwan, et al.. (2019). Comparative Analysis of 3D Shape Recognition in the Presence of Data Inaccuracies. RMIT Research Repository (RMIT University Library). 2017?septe. 2471–2475. 3 indexed citations
16.
Gostar, Amirali Khodadadian, et al.. (2019). State Transition for Statistical SLAM Using Planar Features in 3D Point Clouds. Sensors. 19(7). 1614–1614. 9 indexed citations
17.
Tennakoon, Ruwan, Amirali Khodadadian Gostar, Reza Hoseinnezhad, & Alireza Bab‐Hadiashar. (2018). Retinal fluid segmentation in OCT images using adversarial loss based convolutional neural networks. RMIT Research Repository (RMIT University Library). 1436–1440. 38 indexed citations
18.
Roy, Pallab, Ruwan Tennakoon, Suman Sedai, et al.. (2017). A novel hybrid approach for severity assessment of Diabetic Retinopathy in colour fundus images. 1078–1082. 25 indexed citations
19.
Tennakoon, Ruwan, et al.. (2016). MCMC based sampling technique for robust multi-model fitting and visual data segmentation. RMIT Research Repository (RMIT University Library). 2. 1–6. 3 indexed citations
20.
Bab‐Hadiashar, Alireza, Ruwan Tennakoon, & Marleen de Bruijne. (2013). Quantification of Smoothing Requirement for 3D Optic Flow Calculation of Volumetric Images. IEEE Transactions on Image Processing. 22(6). 2128–2137. 5 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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