scholarly journals Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions

2017 ◽  
Vol 26 (7) ◽  
pp. 3187-3195 ◽  
Author(s):  
Jerome Lapuyade-Lahorgue ◽  
Jing-Hao Xue ◽  
Su Ruan
Keyword(s):  
Hidden Markov ◽  
Statistical Distributions ◽  
Multivariate Statistical ◽  
Markov Fields

scholarly journals Bloking Modification of Gibbs Sampling for Recognition of Hidden Markov Fields

2018 ◽  
pp. 31-41
Author(s):  
Yevgeniy V. Vodolazskiy ◽  
◽  
Serhiy A. Latiuk ◽  
Keyword(s):  
Gibbs Sampling ◽  
Hidden Markov ◽  
Markov Fields

Unsupervised segmentation of hidden Markov fields corrupted by correlated non-Gaussian noise

2018 ◽  
Vol 102 ◽  
pp. 41-59
Author(s):  
Lin An ◽  
Ming Li ◽  
Mohamed El Yazid Boudaren ◽  
Wojciech Pieczynski
Keyword(s):  
Gaussian Noise ◽  
Hidden Markov ◽  
Unsupervised Segmentation ◽  
Markov Fields ◽  
Non Gaussian

scholarly journals MCMC joint separation for the hidden Markov fields with particles

2020 ◽  
Author(s):  
Kevin Williams ◽  
Warren Washer ◽  
Brian Rees ◽  
Agustin Lott
Keyword(s):  
Data Augmentation ◽  
Hidden Markov ◽  
Hidden Variables ◽  
Real Data ◽  
Stationary Regime ◽  
Blind Separation ◽  
Unknown Parameters ◽  
Markov Fields ◽  
Mixing Matrix ◽  
Joint Separation

In this contribution, we consider the problem of the blind separation of noisy instantaneously mixed images. The images are modelized by hidden Markov fields with unknown parameters. Given the observed images, we give a Bayesian formulation and we propose to solve the resulting data augmentation problem by implementing a Monte Carlo Markov Chaîn (MCMC) procedure. We separate the unknown variables into two categories: \\$1$. The parameters of interest which are the mixing matrix, the noise covariance and the parameters of the sources distributions.\\$2$. The hidden variables which are the unobserved sources and the unobserved pixels classification labels.The proposed algorithm provides in the stationary regime samples drawn from the posterior distributions of all the variables involved in the problem leading to a flexibility in the cost function choice.We discuss and characterize some problems of non identifiability and degeneracies of the parameters likelihood and the behavior of the MCMC algorithm in this case. Finally, we show the results for both synthetic and real data to illustrate the feasibility of the proposed solution.

Pairwise and Uniformly Hidden Markov Fields

2009 ◽  
Author(s):  
Wojciech Pieczynski ◽  
George Maroulis ◽  
Theodore E. Simos
Keyword(s):  
Hidden Markov ◽  
Markov Fields

scholarly journals APPLICATION OF CERTAIN FRACTIONAL CALCULUS OPERATORS IN STATISTICAL DISTRIBUTIONS

1996 ◽  
Vol 27 (1) ◽  
pp. 27-36
Author(s):  
R. K. RAINA ◽  
MAMTA BOLIA
Keyword(s):  
Fractional Calculus ◽  
Special Function ◽  
Special Functions ◽  
Characteristic Functions ◽  
Statistical Distributions ◽  
Multivariate Statistical ◽  
Fractional Calculus Operators

This paper deals with the application of certain classes of fractional calculus operators in statistical distributions. The images of product combinations of special functions under the calculus of operators are applied to certain general­ ized forms of univariate and multivariate statistical distributions. Further results giving the expectations, cummulative functions and characteristic functions of such special function distributions are also obtained.

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