This paper describes a probabilistic multiple-hypothesis framework for tracking highly articulated objects. In this framework, the probability density of the tracker state is repr...
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
We investigate the problem of source separation in images in the Bayesian framework using the color channel dependencies. As a case in point we consider the source separation of co...
A probabilistic, ``neural'' approach to sensor modelling and classification is described, performing local data fusion in a wireless system for embedded sensors using a ...
We propose a space-time Markov Random Field (MRF)
model to detect abnormal activities in video. The nodes in
the MRF graph correspond to a grid of local regions in the
video fra...
Jaechul Kim (University of Texas at Austin), Krist...