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» A Markov random field model for term dependencies
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NIPS
2008
14 years 11 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
IH
1998
Springer
15 years 1 months ago
Modelling the False Alarm and Missed Detection Rate for Electronic Watermarks
Theoretical modeling of watermarks allow prediction of the detector reliability and facilitates the development of more reliable systems. In particular, mathematical evaluation is ...
Jean-Paul M. G. Linnartz, Ton Kalker, Geert Depove...
PEWASUN
2005
ACM
15 years 3 months ago
Impact of mobility prediction on the temporal stability of MANET clustering algorithms
Scalability issues for routing in mobile ad hoc networks (MANETs) have been typically addressed using hybrid routing schemes operating in a hierarchical network architecture. Seve...
Aravindhan Venkateswaran, Venkatesh Sarangan, Nata...
CVPR
2009
IEEE
16 years 4 months ago
Observe Locally, Infer Globally: a Space-Time MRF for Detecting Abnormal Activities with Incremental Updates
We propose a space-time Markov Random Field (MRF) model to detect abnormal activities in video. The nodes in the MRF graph correspond to a grid of local regions in the video fra...
Jaechul Kim (University of Texas at Austin), Krist...
CVPR
2008
IEEE
15 years 11 months ago
Simultaneous super-resolution and 3D video using graph-cuts
This paper presents a new method to increase the quality of 3D video, a new media developed to represent 3D objects in motion. This representation is obtained from multi-view reco...
Tony Tung, Shohei Nobuhara, Takashi Matsuyama