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ICCV
1999
IEEE
14 years 11 months ago
A Dynamic Bayesian Network Approach to Figure Tracking using Learned Dynamic Models
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed eit...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham, Ke...
AAAI
1998
13 years 11 months ago
Bayesian Network Models for Generation of Crisis Management Training Scenarios
We present a noisy-OR Bayesian network model for simulation-based training, and an efficient search-based algorithm for automatic synthesis of plausible training scenarios from co...
Eugene Grois, William H. Hsu, Mikhail Voloshin, Da...
WEBI
2010
Springer
13 years 7 months ago
An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors
In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the l...
Jong Gun Lee, Sue Moon, Kavé Salamatian
NIPS
2000
13 years 11 months ago
Learning Switching Linear Models of Human Motion
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. Effective models of human dynamics can be learned from motion capture data usi...
Vladimir Pavlovic, James M. Rehg, John MacCormick
CVPR
2011
IEEE
13 years 6 months ago
Multi-agent event recognition in structured scenarios
We present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activit...
Vlad Morariu, Larry Davis