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ICCV
1999
IEEE
16 years 7 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
15 years 6 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
15 years 3 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
15 years 6 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
15 years 1 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