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...
This paper presents a method for detection of cerebral white matter hyperintensities (WMH) based on run-time PD-, T1-, and T2weighted structural magnetic resonance (MR) images of t...
Charles DeCarli, Christopher Schwarz, Evan Fletche...
We propose a framework for general multiple target tracking, where the input is a set of candidate regions in each frame, as obtained from a state of the art background learning, ...
We investigate a framework where agents search for satisfying products by using referrals from other agents. Our model of a mechanism for transmitting word-of-mouth and the result...
Current outlier detection schemes typically output a numeric score representing the degree to which a given observation is an outlier. We argue that converting the scores into wel...