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» Sequential Learning of Layered Models from Video
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ICIP
2005
IEEE
16 years 1 months ago
Variable module graphs: a framework for inference and learning in modular vision systems
We present a novel and intuitive framework for building modular vision systems for complex tasks such as surveillance applications. Inspired by graphical models, especially factor...
Amit Sethi, Mandar Rahurkar, Thomas S. Huang
MM
2009
ACM
163views Multimedia» more  MM 2009»
15 years 6 months ago
ISP-friendly peer selection in P2P networks
Peer-to-peer (P2P) multicast is a scalable solution adopted by many video streaming systems. However, a prevalence of P2P applications has caused heavy traffic on the Internet. W...
Zhijie Shen, Roger Zimmermann
120
Voted
ICIP
2009
IEEE
16 years 25 days ago
Learning Large Margin Likelihoods For Realtime Head Pose Tracking
We consider the problem of head tracking and pose estimation in realtime from low resolution images. Tracking and pose recognition are treated as two coupled problems in a probabi...
BMCBI
2010
229views more  BMCBI 2010»
14 years 12 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
ICCV
2007
IEEE
16 years 1 months ago
Learning Higher-order Transition Models in Medium-scale Camera Networks
We present a Bayesian framework for learning higherorder transition models in video surveillance networks. Such higher-order models describe object movement between cameras in the...
Ryan Farrell, David S. Doermann, Larry S. Davis