Dynamic Bayesian Networks for Cue Integration

A Bayesian view on multimodal cue integration

Bayesian models of sensory cue integration

Comparing Bayesian models for multisensory cue combination without mandatory integration

Bayesian models of multisensory perception traditionally address the problem of estimating an underlying variable that is assumed to be the cause of the two sensory signals. The brain, however, has to solve a more general problem: it also has to establish which signals come from the same source and should be integrated, and which ones do not and should be segregated. In the last couple of years...

Bayesian Decision Fusion for Dynamic Multi-Cue Object Detection

Visual object detection using single cue information has been successfully applied in various tasks, in particular for near range recognition. While robust classification and probabilistic representation enhance 2D pattern recognition performance, they are ’per se’ restricted due to the limited information content of single cues. The contribution of this work is to demonstrate performance impro...

Dynamic Bayesian Networks for Prognosis

In this paper, a methodology for probabilistic prognosis of a system using a dynamic Bayesian network (DBN) is proposed. Dynamic Bayesian networks are suitable for probabilistic prognosis because of their ability to integrate information in a variety of formats from various sources and give a probabilistic representation of a system. Further, DBNs provide a platform naturally suited for seamles...