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JMLR
2010
129views more  JMLR 2010»
14 years 6 months ago
Expectation Truncation and the Benefits of Preselection In Training Generative Models
We show how a preselection of hidden variables can be used to efficiently train generative models with binary hidden variables. The approach is based on Expectation Maximization (...
Jörg Lücke, Julian Eggert
ICIP
2007
IEEE
16 years 1 months ago
Image Denoising with Nonparametric Hidden Markov Trees
We develop a hierarchical, nonparametric statistical model for wavelet representations of natural images. Extending previous work on Gaussian scale mixtures, wavelet coefficients ...
Jyri J. Kivinen, Erik B. Sudderth, Michael I. Jord...
TMM
2002
117views more  TMM 2002»
14 years 11 months ago
Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models
We present a new statistical model for characterizing texture images based on wavelet-domain hidden Markov models. With a small number of parameters, the new model captures both th...
Minh N. Do, Martin Vetterli
ICIP
2005
IEEE
16 years 1 months ago
Parameter estimation of multi-dimensional hidden Markov models - a scalable approach
Parameter estimation is a key computational issue in all statistical image modeling techniques. In this paper, we explore a computationally efficient parameter estimation algorith...
Dhiraj Joshi, Jia Li, James Ze Wang
ICIP
2004
IEEE
16 years 1 months ago
Stochastic modeling of volume images with a 3-d hidden markov model
Over the years, researchers in the image analysis community have successfully used various statistical modeling methods to segment, classify, and annotate digital images. In this ...
Jia Li, Dhiraj Joshi, James Ze Wang