We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the paramet...
We propose a novel approach for improving level set seg-
mentation methods by embedding the potential functions
from a discriminatively trained conditional random field
(CRF) in...
Dana Cobzas (University of Alberta), Mark Schmidt ...
We propose to increment a statistical shape model with surrogate variables such as anatomical measurements and patient-related information, allowing conditioning the shape distribu...
A robust video object segmentation algorithm for complex conditions in surveillance systems is proposed in this paper. This algorithm contains an unsupervised K-Means background c...
In this paper a general architecture suitable to interface several kinds of sensors for automotive applications is presented. A platform based design approach is pursued to improv...
Luca Fanucci, A. Giambastiani, Francesco Iozzi, Co...