Social robotics research focuses on developing intelligent machines that can perform difficult tasks especially those beyond human capabilities, involving repetitions or adverse conditions. However, robotic vision for high-level recognition associated with reasoning of uncertainty in unstructured environments is still far-fetched compared to the visual comprehension of humans. This dissertation investigates techniques to exploit the rich information from multi-modality sensors in pursuit of extending the frontiers of robotic vision. Three robot-centric recognition tasks are specifically explored; object, scene and action recognition with particular emphasis on designing highly effective and efficient feature representation and recognition algorithms.
|Qualification||Doctor of Philosophy|
|Award date||1 Nov 2017|
|Publication status||Unpublished - 2017|