This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
Human recognition from video requires solving the two tasks, recognition and tracking, simultaneously. This leads to a parameterized time series state space model, representing bo...
We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex...
We present a statistical based non-tree clock distribution construction algorithm that starts with a tree and incrementally insert cross links, such that the skew variation of the...
Wai-Ching Douglas Lam, J. Jam, Cheng-Kok Koh, Venk...
We present a methodology for learning spline-based probabilistic models for sets of contours, proposing a new Monte Carlo variant of the EM algorithm to estimate the parameters of...