Fast algorithms are presented for performing computations in a probabilistic population model. This is a variant of the standard population protocol model--in which finite-state ag...
We propose a novel unsupervised learning framework for activity perception. To understand activities in complicated scenes from visual data, we propose a hierarchical Bayesian mod...
Abstract. We present an automated approach to the problem of connectivity-based partitioning of brain structures using diffusion imaging. White-matter fibres connect different area...
Philip A. Cook, Hui Zhang 0005, Brian B. Avants, P...
A probabilistic framework for vision based volumetric reconstruction and marker free tracking of hand and face volumes is presented, which exclusively relies on off-the-shelf hardw...
Christoph John, Ulrich Schwanecke, Holger Regenbre...
Abstract. The problem of interpreting Natural Language (NL) discourse is generally of exponential complexity. However, since interactions with users must be conducted in real time,...