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SAC
2008
ACM
15 years 5 months ago
Particle methods for maximum likelihood estimation in latent variable models
Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants. Our approach is ...
Adam M. Johansen, Arnaud Doucet, Manuel Davy
CVPR
2009
IEEE
1216views Computer Vision» more  CVPR 2009»
17 years 1 months ago
Marked Point Processes for Crowd Counting
A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placem...
Robert T. Collins, Weina Ge
CORR
2008
Springer
111views Education» more  CORR 2008»
15 years 4 months ago
Concave Programming Upper Bounds on the Capacity of 2-D Constraints
The capacity of 1-D constraints is given by the entropy of a corresponding stationary maxentropic Markov chain. Namely, the entropy is maximized over a set of probability distribut...
Ido Tal, Ron M. Roth
ICST
2010
IEEE
15 years 4 months ago
Generating Transition Probabilities for Automatic Model-Based Test Generation
—Markov chains with Labelled Transitions can be used to generate test cases in a model-based approach. These test cases are generated by random walks on the model according to pr...
Abderrahmane Feliachi, Hélène Le Gue...
ICIP
2010
IEEE
15 years 4 months ago
Instrument parameter estimation in bayesian convex deconvolution
This paper proposes a Bayesian approach for estimation of instrument parameter in convex image deconvolution. The parameters of the instrument response (PSF) are jointly estimated...
François Orieux, Thomas Rodet, Jean-Fran&cc...