11 years 6 months ago
An Object-Based Bayesian Framework for Top-Down Visual Attention
We introduce a new task-independent framework to model top-down overt visual attention based on graphical models for probabilistic inference and reasoning. We describe a Dynamic B...
Ali Borji, Dicky N. Sihite, Laurent Itti
12 years 8 months ago
Hidden Discrete Tempo Model: A tempo-aware timing model for audio-to-score alignment
In this paper, we present the Hidden Discrete Tempo Model, an effective Dynamic Bayesian Network for audio to score matching. Its main feature is an explicit modeling of tempo, wh...
Cyril Joder, Slim Essid, Gaël Richard
131views more  CJ 2010»
13 years 1 months ago
Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks
an be used to abstract away from the physical reality by describing it as components that exist in discrete states with probabilistically invoked actions that change the state. The...
Duncan Gillies, David Thornley, Chatschik Bisdikia...
13 years 2 months ago
Hybrid HMM/BLSTM-RNN for Robust Speech Recognition
The question how to integrate information from different sources in speech decoding is still only partially solved (layered architecture versus integrated search). We investigate t...
Yang Sun, Louis ten Bosch, Lou Boves
91views more  COMPSEC 2004»
13 years 4 months ago
Predicting the intrusion intentions by observing system call sequences
Identifying the intentions or attempts of the monitored agents through observations is very vital in computer network security. In this paper, a plan recognition method for predict...
Li Feng, Xiaohong Guan, Sangang Guo, Yan Gao, Pein...
137views more  PAMI 2008»
13 years 4 months ago
Tracking the Visual Focus of Attention for a Varying Number of Wandering People
In this article, we define and address the problem of finding the visual focus of attention for a varying number of wandering people (VFOA-W)
Kevin Smith, Sileye O. Ba, Jean-Marc Odobez, Danie...
229views more  BMCBI 2010»
13 years 4 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
13 years 5 months ago
Dynamic Bayesian Networks for Brain-Computer Interfaces
We describe an approach to building brain-computer interfaces (BCI) based on graphical models for probabilistic inference and learning. We show how a dynamic Bayesian network (DBN...
Pradeep Shenoy, Rajesh P. N. Rao
13 years 5 months ago
Discovering Weakly-Interacting Factors in a Complex Stochastic Process
Dynamic Bayesian networks are structured representations of stochastic processes. Despite their structure, exact inference in DBNs is generally intractable. One approach to approx...
Charlie Frogner, Avi Pfeffer
13 years 5 months ago
Online Analysis of Hierarchical Events in Meetings
Automatic online analysis of meetings is very important from three points of view: serving as an important archive of a meeting, understanding human interaction processes, and prov...
Xiang Zhang, Guangyou Xu, Xiaoling Xiao, Linmi Tao