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.
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.
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
, Hawaii, January 2019, pp. 254--262.
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.
There are several systems that use one or several Kinect sensors for human gait analysis, particularly for diagnosis of patients. However, due to the limited depth sensing range of the Kinect-a sensor manufactured for video gaming-the depth measurement accuracy reduces with distance from the Kinect. In addition, self-occlusion of the subject limits the accuracy and utility of such systems. We overcome these limitations by first by using a two-Kinect gait analysis system and second by mechanically moving the Kinects in synchronization with the test subject and each other. These methods increase the practical measurement range of the Kinectbased system whilst maintaining the measurement accuracy.
Madhura Pathegama, Dileepa Marasinghe, Kanishka Wijayasekara, Ishan Karunanayake, Chamira Edussooriya, Pujitha Silva, and Ranga Rodrigo, "Moving Kinect-Based Gait Analysis with Increased Range
," in Proceedings of IEEE International Conference on Systems, Man, and Cybernetics
, Miyazaki, Japan, October 2018, pp. 4126--4131.
Ravindu Kumarasiri, Akila Niroshan, Zaman Lantra, Thanuja Madusanka, Chamira Edussooriya, and Ranga Rodrigo, "Gait Analysis Using RGBD Sensors
," in Proceedings of International Conference on
Control, Automation, Robotics and Vision
, Singapore, November 2018, pp. 460--465.
See research for more details on projects. See publications for a list of publications.