Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
Virtually all methods of learning dynamic systems from data start from the same basic assumption: the learning algorithm will be given a sequence of data generated from the dynami...
While traditional face recognition is typically based on still images, face recognition from video sequences has become popular recently. In this paper, we propose to use adaptive...
In this paper, we present a novel occlusion detection scheme for hidden Markov modeled shapes. First, hidden Markov model (HMM) is built using multiple examples of the shape. A re...
Abstract. We investigate the combination of propositional SAT checkers with domain-specific theorem provers as a foundation for bounded model checking over infinite domains. Given ...