In this paper we propose and test an action recognition algorithm in which the images of the scene captured by a significant number of cameras are first used to generate a volumet...
In this work we present a novel multi-modal mixed-state dynamic Bayesian network (DBN) for robust meeting event classification. The model uses information from lapel microphones,...
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitori...
Abstract. This work aims to recognize signs which have both manual and nonmanual components by providing a sequential belief-based fusion mechanism. We propose a methodology based ...
Oya Aran, Thomas Burger, Alice Caplier, Lale Akaru...
Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other task...