Kester Duncan

Learn More
— Rapidly acquiring the shape and pose information of unknown objects is an essential characteristic of modern robotic systems in order to perform efficient manipulation tasks. In this work, we present a framework for 3D geometric shape recovery and pose estimation from unorganized point cloud data. We propose a low latency multi-scale voxelization strategy(More)
We present a probabilistic framework to automatically learn models of recurring signs from multiple sign language video sequences containing the vocabulary of interest. We extract the parts of the signs that are present in most occurrences of the sign in context and are robust to the variations produced by adjacent signs. Each sentence video is first(More)
In order for assistive robots to collaborate effectively with humans, they must be endowed with the ability to perceive scenes and more importantly, recognize human intentions. These intentions are often inferred from observed physical actions and direct communication from fully-functional individuals. For individuals with reduced capabilities , it may be(More)
  • 1