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» Parametric Process Model Inference
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ICIP
2005
IEEE
14 years 7 months ago
SAR images as mixtures of Gaussian mixtures
We consider the problem of image segmentation by clustering local histograms with parametric mixture-of-mixture models. These models represent each cluster by a single mixture mod...
Peter Orbanz, Joachim M. Buhmann
ICML
2004
IEEE
14 years 7 months ago
Variational methods for the Dirichlet process
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
David M. Blei, Michael I. Jordan
ICIP
2008
IEEE
14 years 7 months ago
Photon-limited image denoising by inference on multiscale models
We present an improved statistical model of Poisson processes, with applications in photon-limited imaging. We build on previous work, adopting a multiscale representation of the ...
Stamatios Lefkimmiatis, George Papandreou, Petros ...
ICASSP
2011
IEEE
12 years 9 months ago
Robust parametrization for non-destructive evaluation of composites using ultrasonic signals
Anticipating and characterizing damages in layered carbon fiberreinforced polymers is a challenging problem. Non-destructive evaluation using ultrasonic signals is a well-establi...
Nicolas Bochud, Angel M. Gomez, Guillermo Rus, Jos...
KDD
2007
ACM
237views Data Mining» more  KDD 2007»
14 years 6 months ago
Knowledge discovery of multiple-topic document using parametric mixture model with dirichlet prior
Documents, such as those seen on Wikipedia and Folksonomy, have tended to be assigned with multiple topics as a meta-data. Therefore, it is more and more important to analyze a re...
Issei Sato, Hiroshi Nakagawa