Abstract. This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an ...
The ability to detect and track human heads and faces in video sequences is useful in a great number of applications, such as human-computer interaction and gesture recognition. Re...
We consider how tracking in stereo may be enhanced by coupling pairs of active contours in different views via affine epipolar geometry and various subsets of planar affine transf...
For interaction with its environment, a robot is required to learn models of objects and to perceive these models in the livestreams from its sensors. In this paper, we propose a ...
We introduce novel discriminative learning algorithms for dynamical systems. Models such as Conditional Random Fields or Maximum Entropy Markov Models outperform the generative Hi...