We describe a hidden Markov modeling approach to multiple change-points that has attractive computational and statistical properties. This approach yields explicit recursive filter...
Abstract. This paper presents a novel probabilistic approach to integrating multiple cues in visual tracking. We perform tracking in different cues by interacting processes. Each p...
We present a novel approach to edge detection in bar code signals using a hidden Markov model (HMM). We also present an algorithm for selection of an optimal filter scale used in ...
We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at mul...
We present a semi-supervised source separation methodology to denoise speech by modeling speech as one source and noise as the other source. We model speech using the recently pro...