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» Explaining inferences in Bayesian networks
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ICCV
2009
IEEE
6637views Computer Vision» more  ICCV 2009»
15 years 21 days ago
A Markov Clustering Topic Model for Mining Behaviour in Video
This paper addresses the problem of fully automated mining of public space video data. A novel Markov Clustering Topic Model (MCTM) is introduced which builds on existing Dynami...
Timothy Hospedales, Shaogang Gong, Tao Xiang
ECCV
2006
Springer
13 years 9 months ago
Robust Face Alignment Based on Hierarchical Classifier Network
Abstract. Robust face alignment is crucial for many face processing applications. As face detection only gives a rough estimation of face region, one important problem is how to al...
Li Zhang, Haizhou Ai, Shihong Lao
VTC
2007
IEEE
133views Communications» more  VTC 2007»
14 years 2 months ago
An Architecture for Situation-Aware Driver Assistance Systems
Current Driver Assistance Systems merely use a minimum set of information. By using additional information of the environment hazardous situations can be detected earlier, more re...
Matthias Röckl, Patrick Robertson, Korbinian ...
ICML
2010
IEEE
13 years 8 months ago
Continuous-Time Belief Propagation
Many temporal processes can be naturally modeled as a stochastic system that evolves continuously over time. The representation language of continuous-time Bayesian networks allow...
Tal El-Hay, Ido Cohn, Nir Friedman, Raz Kupferman
CORR
2011
Springer
172views Education» more  CORR 2011»
12 years 11 months ago
Uncovering the Temporal Dynamics of Diffusion Networks
Time plays an essential role in the diffusion of information, influence and disease over networks. In many cases we only observe when a node copies information, makes a decision ...
Manuel Gomez-Rodriguez, David Balduzzi, Bernhard S...