We present a novel mixed-state dynamic Bayesian network (DBN) framework for modeling and classifying timeseries data such as object trajectories. A hidden Markov model (HMM) of di...
Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huan...
We propose a novel, non-simulative, probabilistic model for switching activity in sequential circuits, capturing both spatio-temporal correlations at internal nodes and higher ord...
Sanjukta Bhanja, Karthikeyan Lingasubramanian, N. ...
Dynamic Bayesian Networks (DBNs) have been widely studied in multi-modal speech recognition applications. Here, we introduce DBNs into an acoustically-driven talking face synthesi...
Jianxia Xue, Jonas Borgstrom, Jintao Jiang, Lynne ...
Particle filtering algorithms can be used for the monitoring of dynamic systems with continuous state variables and without any constraints on the form of the probability distribu...
Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous mu...
Vladimir Pavlovic, James M. Rehg, Ashutosh Garg, T...