Publications

We are interested in building resource efficient intelligent systems, mainly performing intelligent tasks on image data such as medical imaging or video recordings. Here are some of the projects we’ve been working on.

Medical Image Analysis and Diagnostic Assistance

We work on image data such as X-Ray, CT, Electron Microscopy as well as Dermoscopic images. Our efforts are to create diagnostic assistive tools using computer visions methods that can analyze above types of images so that an informative judgement can be made by medical practitioners using the output of such methods. Following are some of our publications in this area

Amarasinghe, N. H., & Ambegoda, T. D. (2024). Few-Shot Lung Cancer Classification Using Prototypical Networks. In 2024 4th International Conference on Advanced Research in Computing (ICARC) (pp. 79-84). IEEE.
Nimalsiri, W., Hennayake, M., Rathnayake, K., Ambegoda, T. D., & Meedeniya, D. (2023). Automated radiology report generation using transformers. In 2023 3rd International Conference on Advanced Research in Computing (ICARC) (pp. 90-95). IEEE.
Nimalsiri, W., Hennayake, M., Rathnayake, K., Ambegoda, T. D., & Meedeniya, D. (2023). Cxlseg dataset: Chest x-ray with lung segmentation. In 2023 International Conference On Cyber Management And Engineering (CyMaEn) (pp. 327-331). IEEE.
Dasanayaka, S., Shantha, V., Silva, S., Meedeniya, D., & Ambegoda, T. (2022). Interpretable machine learning for brain tumour analysis using MRI and whole slide images. Software Impacts, 13, 100340.
Wijerathna, V., Raveen, H., Abeygunawardhana, S., & Ambegoda, T. D. (2022). Chest x-ray caption generation with chexnet. In 2022 Moratuwa Engineering Research Conference (MERCon) (pp. 1-6). IEEE.
Silva, K., Maheepala, T., Tharaka, K., & Ambegoda, T. D. (2022). Adversarial Learning to Improve Question Image Embedding in Medical Visual Question Answering. In 2022 Moratuwa Engineering Research Conference (MERCon) (pp. 1-6). IEEE.
Dasanayaka, S., Silva, S., Shantha, V., Meedeniya, D., & Ambegoda, T. (2022). Interpretable machine learning for brain tumor analysis using MRI. In 2022 2nd International Conference on Advanced Research in Computing (ICARC) (pp. 212-217). IEEE.
Hussaindeen, A., Iqbal, S., & Ambegoda, T. D. (2022). Multi-label prototype based interpretable machine learning for melanoma detection. International Journal Of Advances In Signal And Image Sciences, 8(1), 40-53.

Satellite Image Analysis / Remote Sensing

In our research on satellite image analysis using computer vision, we explore Mars's surface to better understand its geology and potential for past life. One project involves segmenting serpentine zones on Mars, which helps in studying the planet's geological history. Another focuses on automatically detecting and segmenting non-inverted channels from satellite data, shedding light on Mars's ancient water flow. These efforts aim to provide insights into Mars's environment and support future exploration

Malaviarachchi, S. P. K., Dharmapriya, P., Chandrajith, R., Pitawala, H. M. T. G. A., Karunatillake, S., Hughes, E., Vithanage, M., Edussuriya, T., Ambegoda, T., Anandakiththi, K., & others. (2024). Overview of Sri Lanka’s Rare Occurrence of Serpentinites Within Proterozoic High-Grade Metamorphic Basement Rocks as a Mars-Context Research Site. LPI Contributions, 3040, 2324.
Jayakody, D., & Ambegoda, T. (2024). Few-shot Multispectral Segmentation with Representations Generated by Reinforcement Learning. In 35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024.
Kavinda, K. P. G., Rathnayaka, C. B., Silva, W. G. C., Ambegoda, T. D., Manogaran, R., & Karunatillake, S. (2023). RESIST: Tool to Automatically Segment Martian Inverted Channels in HiRISE Images. In 54th Lunar and Planetary Science Conference (Vol. 2806, pp. 1821).
Jayakody, D. R., Ambegoda, T. D., Karunatillake, S., & Hughes, E. B. (2023). Optimized Field Sampling of Mars-Analog Serpentine Zones via Machine Learning. In 54th Lunar and Planetary Science Conference (Vol. 2806, pp. 2242).
Rathnayaka, C., Silva, G., Pathirana, K., Ambegoda, T., Manogaran, R., & Karunatillake, S. (2023). RESIST: Resource Efficient Satellite Image Segmentation Tool for Curvilinear Structure Segmentation. In 2023 IEEE International Conference on Image Processing (ICIP).
Jayakody, N., Cooray, P., Dasanayake, S., & Ambegoda, T. (2023). Monocular Depth Estimation of Planetary Landforms: A Diffusion Model Approach for Faster Inference. In 2023 IEEE International Conference on Image Processing (ICIP).

Sensors and Data Science

There are a variety of sensors to capture events in the environment including machines and humans. These could be IMU sensors to detect acceleration, lidar sensors to sense the 3D environment and its changes, wifi transceivers that can capture certain changes in the environment, and vibration sensors that can capture subtle changes in machine vibrations when they are in operation . Our efforts are to develop and apply data science techniques to extract rich features and use them to build tools to support decision making for humans as well as autonomous agents such as robots

Schmidt, T., Ambegoda, T., & Gunasekera, K. (2023). Vehicle Classification Using Raspberry Pi: A Guide to Capturing WiFi CSI Data. In 2023 Moratuwa Engineering Research Conference (MERCon) (pp. 702-707). IEEE.
Wijesinghe, A., Ambegoda, T. D., Perera, A. S., & Sivakumar, T. (2023). Siamese networks for RF-based vehicle trajectory prediction. In 2023 IEEE 17th International Conference on Industrial and Information Systems (ICIIS) (pp. 495-500). IEEE.

Sign Language Recognition

Our aim is to build an automatic realtime sign language recognition system for the Sinhala Sign Language that's used in Sri Lanka. We have developed tools to recognize the Sinhala Sign Alphabet as well as a number of words. Currently we are trying to expand the vocabulary as well as the capability to translate sentences in Sinhala Sign Language in realtime. We have already contributed the largest dataset for this purpose and currently working on expanding it further to support similar research and development efforts.

Sarveswarasarma, P., Sathulakjan, T., Godfrey, V. J. V., & Ambegoda, T. D. (2024). Air Signing and Privacy-Preserving Signature Verification for Digital Documents. arXiv preprint arXiv:2405.10868.
Charuka, K., Wickramanayake, S., Ambegoda, T. D., Madhushan, P., & Wijesooriya, D. (2023). Sign Language Recognition for Low Resource Languages Using Few Shot Learning. In International Conference on Neural Information Processing (pp. 203-214). Springer.
Weerasooriya, A. A., & Ambegoda, T. D. (2022). Sinhala fingerspelling sign language recognition with computer vision. In 2022 Moratuwa Engineering Research Conference (MERCon) (pp. 1-6). IEEE.

Smart Agriculture

IoT and ML methods have a significant scope to make crop monitoring efficient and effective. We have been working on developing cost effective tools and frameworks to monitor the environment of crops and their growth rate with sensor networks and computer vision methods.

Senevirathne, I., Ambegoda, T., Wijesena, R., & Perera, I. (2022). IoT-based soil nutrient analyser using Gaussian process regression. In 2022 2nd International Conference on Advanced Research in Computing (ICARC) (pp. 7-12). IEEE.

Robotics and IoT with AI

Robotics and IoT systems involve processing of sensor data in order to perform analysis of events that lead to better decisions either by humans or autonomous agents such as mobile robots. We mainly work on real-time object detection with cameras/lidars as well as processing of other sensor data such as Inertial Motion Unit (IMU)

Nordt, J., Ambegoda, T., & Chen, B. (2020). Cleaning apparatus and method for operating a cleaning apparatus. US Patent App. 16/764,144.

Document Analysis and Digitization

Herath, D., Dinuwan, C., Ihalagedara, C., & Ambegoda, T. (2024). Enhancing Educational Outcomes Through AI Powered Learning Strategy Recommendation System. International Journal of Advanced Computer Science & Applications, 15(10).

Computer Vision and Machine Learning

Sivakumar, P., Janson, P., Rajasegaran, J., & Ambegoda, T. (2024). FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation. arXiv preprint arXiv:2408.04803.
Kanagarajah, S., Ambegoda, T., & Rodrigo, R. (2023). SATHUR: Self Augmenting Task Hallucinal Unified Representation for Generalized Class Incremental Learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3473-3480).
Ambegoda, T. D., & Cook, M. (2020). Efficient 2D neuron boundary segmentation with local topological constraints. arXiv preprint arXiv:2002.01036.
Ambegoda, T. D., Martel, J. N. P., Adamcik, J., Cook, M., & Hahnloser, R. H. R. (2020). Estimation of z-thickness and xy-anisotropy of electron microscopy images using gaussian processes. arXiv preprint arXiv:2002.00228.

Other Publications

Minoli, M., & Ambegoda, T. D. (2023). Knowledge Graphs for COVID-19: A Survey. In Advanced AI and Internet of Health Things for Combating Pandemics (pp. 3-19). Springer.
Ambegoda Liyana Arachchige, T. D. (2017). EM image analysis for neuron segmentation, thickness estimation, and synaptic bouton quantification. PhD Thesis, ETH Zurich.
Ambegoda, A. L. A. T. D., De Silva, W. T. S., Hemachandra, K. T., Samarasinghe, T. N., & Samarasinghe, A. T. L. K. (2008). Centralized traffic controlling system for Sri Lanka railways. In 2008 4th International Conference on Information and Automation for Sustainability (pp. 145-149). IEEE.