Hidden Markov models assume that observations in time series data stem from some hidden process that can be compactly represented as a Markov chain. We generalize this model by as...
We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattain...
Alphan Altinok, Motaz A. El Saban, Austin J. Peck,...
We are interested in recovering aspects of vocal tract’s geometry and dynamics from auditory and visual speech cues. We approach the problem in a statistical framework based on ...
Athanassios Katsamanis, George Papandreou, Petros ...
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...
In the paper we combine a Bayesian Network model for encoding forensic evidence during a given time interval with a Hidden Markov Model (EBN-HMM) for tracking and predicting the de...
Olivier Y. de Vel, Nianjun Liu, Terry Caelli, Tib&...