Sciweavers

Share
7 search results - page 1 / 2
» Boosting and Structure Learning in Dynamic Bayesian Networks...
Sort
View
ICPR
2002
IEEE
12 years 6 months ago
Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection
Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with ef?cient algorithms for inference and learning. Ear...
Tanzeem Choudhury, James M. Rehg, Vladimir Pavlovi...
FGR
2000
IEEE
147views Biometrics» more  FGR 2000»
11 years 9 months ago
Audio-Visual Speaker Detection Using Dynamic Bayesian Networks
Ashutosh Garg, Vladimir Pavlovic, James M. Rehg
CVPR
2000
IEEE
12 years 7 months ago
Multimodal Speaker Detection Using Error Feedback Dynamic Bayesian Networks
Design and development of novel human-computer interfaces poses a challenging problem: actions and intentions of users have to be inferred from sequences of noisy and ambiguous mu...
Vladimir Pavlovic, James M. Rehg, Ashutosh Garg, T...
ICMCS
2008
IEEE
207views Multimedia» more  ICMCS 2008»
11 years 11 months ago
Structure learning in a Bayesian network-based video indexing framework
Several stochastic models provide an effective framework to identify the temporal structure of audiovisual data. Most of them need as input a first video structure, i.e. connecti...
Siwar Baghdadi, Guillaume Gravier, Claire-Hé...
BMCBI
2010
229views more  BMCBI 2010»
11 years 5 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
books