We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the...
We present a new unsupervised method to learn unified probabilistic object models (POMs) which can be applied to classification, segmentation, and recognition. We formulate this a...
Yuanhao Chen, Long Zhu, Alan L. Yuille, HongJiang ...
We consider the problem of learning to map between two vector spaces given pairs of matching vectors, one from each space. This problem naturally arises in numerous vision problem...
Many man-made and natural structures consist of similar elements arranged in regular patterns. In this paper we present an unsupervised approach for discovering and reasoning on re...
The automatic detection of lung nodules attached to other pulmonary structures is a useful yet challenging task in lung CAD systems. In this paper, we propose a stratified statist...
Dijia Wu, Le Lu, Jinbo Bi, Yoshihisa Shinagawa, Ki...