In this paper we present a novel method for foreground segmentation. Our proposed approach follows a nonparametric background modeling paradigm, thus the background is modeled by ...
Martin Hofmann 0011, Philipp Tiefenbacher, Gerhard...
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
This paper presents and compares 3 methods for the statistical localization of partially occulted landmarks. In many real applications, some information is visible in images and s...
Barbara Romaniuk, Michel Desvignes, Marinette Reve...
In this paper, we present an approach for measuring certain properties of synthetic optimization problems based on the assumed distribution of coefficient values. We show how to e...
Background: With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis...