Local image descriptors that are highly discriminative,
computational efficient, and with low storage footprint have
long been a dream goal of computer vision research. In this
...
In this paper, we present a novel on-line probabilistic generative model that simultaneously deals with both the clustering and the tracking of an unknown number of moving objects...
Boundary detection constitutes a crucial step in many computer vision tasks. We present a novel learning approach to automatically construct a boundary detector for natural images...
Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP...
We introduce the controlled predictive linearGaussian model (cPLG), a model that uses predictive state to model discrete-time dynamical systems with real-valued observations and v...