This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...
Copulas have attracted much attention in spatial statistics over the past few years. They are used as a flexible alternative to traditional methods for nonGaussian spatial modelin...
The objective of this work is to interpret inductive results obtained by the unsupervised learning method OSHAM. We briefly introduce the learning process of OSHAM, that extracts ...
This paper proposes a new Bayesian framework for solving the matting problem, i.e. extracting a foreground element from a background image by estimating an opacity for each pixel ...
Yung-Yu Chuang, Brian Curless, David Salesin, Rich...
We study the detection performance of large scale sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards th...