Dual hidden Markov model characterization of wavelet coefficients from multiaspect scattering data

2000 ◽  
Author(s):  
Nilanjan Dasgupta ◽  
Paul R. Runkle ◽  
Luise S. Couchman ◽  
Lawrence Carin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Scattering Data ◽  
Wavelet Coefficients

Dual hidden Markov model for characterizing wavelet coefficients from multi-aspect scattering data

2001 ◽  
Vol 81 (6) ◽  
pp. 1303-1316 ◽  
Author(s):  
Nilanjan Dasgupta ◽  
Paul Runkle ◽  
Luise Couchman ◽  
Lawrence Carin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Scattering Data ◽  
Wavelet Coefficients

scholarly journals Face Recognition Using Wavelet Coefficients and Hidden Markov Model

2003 ◽  
Vol 13 (6) ◽  
pp. 673-678 ◽  
Author(s):  
Kyung-Ah Lee ◽  
Dae-Jong Lee ◽  
Jang-Hwan Park ◽  
Myung-Geun Chun
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Wavelet Coefficients

scholarly journals Simultaneous characterization of sense and antisense genomic processes by the double-stranded hidden Markov model

2015 ◽  
Vol 44 (5) ◽  
pp. e44-e44 ◽  
Author(s):  
Julia Glas ◽  
Sebastian Dümcke ◽  
Benedikt Zacher ◽  
Don Poron ◽  
Julien Gagneur ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov

SIGNAL DENOISING USING WAVELETS AND BLOCK HIDDEN MARKOV MODEL

2005 ◽  
Vol 19 (05) ◽  
pp. 681-700 ◽  
Author(s):  
ZHIWU LIAO ◽  
Y. Y. TANG
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Mean Squared Error ◽  
Hidden Markov ◽  
Wavelet Coefficient ◽  
Gaussian Mixture ◽  
Signal Denoising ◽  
Wavelet Domain ◽  
Wavelet Coefficients ◽  
New Framework

This paper presents a new framework for signal denoising based on wavelet-domain hidden Markov models (HMMs). The new framework enables us to concisely model the statistical dependencies and non-Gaussian statistics encountered in real-world signals, and enables us to get a more reliable and local model using blocks. Wavelet-domain HMMs are designed with the intrinsic properties of wavelet transform and provide powerful yet tractable probabilistic signal models. In this paper, we propose a novel wavelet domain HMM using blocks to strike a delicate balance between improving spatial adaptability of contextual HMM (CHMM) and modeling a more reliable HMM. Each wavelet coefficient is modeled as a Gaussian mixture model, and the dependencies among wavelet coefficients in each subband are described by a context structure, then the structure is modified by blocks which are connected areas in a scale conditioned on the same context. Before denoising a signal, efficient Expectation Maximization (EM) algorithms are developed for fitting the HMMs to observational signal data. Parameters of trained HMM are used to modify wavelet coefficients according to the rule of minimizing the mean squared error (MSE) of the signal. Then, reverse wavelet transformation is utilized to modified wavelet coefficients. Finally, experimental results are given. The results show that block hidden Markov model (BHMM) is a powerful yet simple tool in signal denoising.

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