Many low-level vision algorithms assume a prior probability over images, and there has been great interest in trying to learn this prior from examples. Since images are very non G...
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...
In this paper, we introduce a novel algorithm to solve
global shape registration problems. We use gray-scale “images”
to represent source shapes, and propose a novel twocompo...
Hongsheng Li (Lehigh University), Tian Shen (Lehig...
This paper presents a generative model based approach to man-made structure detection in 2D natural images. The proposed approach uses a causal multiscale random field suggested i...