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 address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider obje...
We propose a method to improve approximate inference methods by correcting for the influence of loops in the graphical model. The method is a generalization and alternative implem...
When dealing with sensors with different time resolutions, it is desirable to model a sensor reading as pertaining to a time interval rather than a unit of time. We introduce two ...
Sander Evers, Maarten M. Fokkinga, Peter M. G. Ape...