Welcome to Ranga Rodrigo's web site.

I am a senior lecturer at the Department of Electronic and Telecommunication Engineering at the University of Moratuwa, Sri Lanka. I work in the area of computer vision. Within this, survaillance, scene understanding, tracking, and activity recognition are of particular interest. We extensively use deep learning for our work. Our current work includes learning in robotics, making deep networks more effective by exploring new architectures, developing new routing algorithms, and improving convolution layers.

Board Games: Learning beyond Simulations

Reinforcement learning algorithms have been successfully trained for games like GO, Atari, and Chess in simulated environments. However, in cue sport-based games like Carrom, real world is unpredictable unlike in Chess and GO due to the stochastic nature of the gameplay as well as the effect of external factors such as friction combined with multiple collisions. Hence, solely training in a simulated platform for games like Billiard and Carrom, which need precise execution of a shot, would not be ideal in actual gameplay. This paper presents a real-time vision based efficient robotic system to play Carrom against a proficient human opponent. We demonstrate the challenges of adopting a reinforcement learning algorithm beyond simulations in implementing strategic gameplay for the robotic system. We currently achieve an overall shot accuracy of 70.6% by combining heuristic and reinforcement learning algorithms. Analysis of the overall results suggests the possibility of adopting a realworld training for board games which need precise mechanical actuation beyond simulations.

Naveen Karunanayake, Achintha Wijesinghe, Chameera Wijethunga, Chinthani Kumaradasa, Peshala Jayasekara, and Ranga Rodrigo, "Towards a Smart Opponent for Board Games: Learning beyond Simulations," in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, Toronto, CA (virtual), 2020, pp. 1--8.

Context-Aware Occlusion Removal

In this work, we identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently. We detect occlusions by considering the relation between foreground and background object classes represented by vector embeddings, and removes them through inpainting. Notice how the skier has been automatically removed.

Kumara Kahatapitiya, Dumindu Tissera, and Ranga Rodrigo, "Context-Aware Automatic Occlusion Removal," in Proceedings of IEEE International Conference on Image Processing, Taipei, Taiwan, September 2019, pp. 1--4.
URL: https://arxiv.org/abs/1905.02710

Extensions to Capsule Networks

We extended the capsule networks taking several paths. In the TextCaps work, we adjust the instantiation parameters with random controlled noise to generate new training samples from the existing samples, with realistic augmentations which reflect actual variations that are present in human hand writing. Our results with a mere 200 training samples per class surpass existing character recognition results in MNIST and several other datasets. In DeepCaps we developed a deep capsule network architecture which uses a novel 3D convolution based dynamic routing algorithm. Further, we propose a class-independent decoder network, which strengthens the use of reconstruction loss as a regularization term. This leads to an interesting property of the decoder, which allows us to identify and control the physical attributes of the images represented by the instantiation parameters.

Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Jathushan Rajasegaran, Suranga Seneviratne, and Ranga Rodrigo, "TextCaps: Handwritten Character Recognition With Very Small Datasets," in Proceedings of IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, January 2019, pp. 254--262.
URL: https://ieeexplore.ieee.org/abstract/document/8658735
Jathushan Rajasegaran, Vinoj Jayasundara, Sandaru Jayasekara, Hirunima Jayasekara, Suranga Seneviratne, and Ranga Rodrigo, "DeepCaps: Going Deeper with Capsule Networks," in Proceedings of IEEE CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, June 2019, pp. 1--9.
URL: https://arxiv.org/abs/1904.09546

See research for more details on projects. See publications for a list of publications.