Statistical Wavelet Subband Characterization Based on Generalized Gamma Density and Its Application in Texture Retrieval

2010 ◽  
Vol 19 (2) ◽  
pp. 281-289 ◽  
Author(s):  
S.K. Choy ◽  
C.S. Tong
Keyword(s):  
Generalized Gamma ◽  
Gamma Density ◽  
Texture Retrieval

Image Denoising via Bayesian Estimation of Statistical Parameter Using Generalized Gamma Density Prior in Gaussian Noise Model

2015 ◽  
Vol 14 (02) ◽  
pp. 1550017
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Image Processing ◽  
Image Denoising ◽  
Gaussian Noise ◽  
Classical Problem ◽  
Statistical Parameter ◽  
Noise Model ◽  
Natural Image ◽  
Bayesian Techniques ◽  
Generalized Gamma ◽  
Gamma Density

The application of image processing in industry has shown remarkable success over the last decade, for example, in security and telecommunication systems. The denoising of natural image corrupted by Gaussian noise is a classical problem in image processing. So, image denoising is an indispensable step during image processing. This paper is concerned with dual-tree complex wavelet-based image denoising using Bayesian techniques. One of the cruxes of the Bayesian image denoising algorithms is to estimate the statistical parameter of the image. Here, we employ maximum a posteriori (MAP) estimation to calculate local observed variance with generalized Gamma density prior for local observed variance and Laplacian or Gaussian distribution for noisy wavelet coefficients. Evidently, our selection of prior distribution is motivated by efficient and flexible properties of generalized Gamma density. The experimental results show that the proposed method yields good denoising results.

Generalized gamma density-based score functions for fast and flexible ICA

2007 ◽  
Vol 87 (5) ◽  
pp. 1156-1162 ◽  
Author(s):  
Kostas Kokkinakis ◽  
Asoke K. Nandi
Keyword(s):  
Score Functions ◽  
Generalized Gamma ◽  
Gamma Density

scholarly journals Mixture of Generalized Gamma Density-Based Score Function for Fastica

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
M. EL-Sayed Waheed ◽  
Osama Abdo Mohamed ◽  
M. E. Abd El-aziz
Keyword(s):  
Score Function ◽  
Blind Signal Separation ◽  
Score Functions ◽  
Generalized Gamma ◽  
Blind Signal ◽  
Gamma Density ◽  
Wide Range ◽  
The Family ◽  
Source Signals ◽  
Derivative Information

We propose an entirely novel family of score functions for blind signal separation (BSS), based on the family of mixture generalized gamma density which includes generalized gamma, Weilbull, gamma, and Laplace and Gaussian probability density functions. To blindly extract the independent source signals, we resort to the FastICA approach, whilst to adaptively estimate the parameters of such score functions, we use Nelder-Mead for optimizing the maximum likelihood (ML) objective function without relaying on any derivative information. Our experimental results with source employing a wide range of statistics distribution show that Nelder-Mead technique produce a good estimation for the parameters of score functions.

scholarly journals On the Accuracy of the Generalized Gamma Approximation to Generalized Negative Binomial Random Sums

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1571
Author(s):  
Irina Shevtsova ◽  
Mikhail Tselishchev
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Law Of Large Numbers ◽  
Negative Binomial ◽  
Infinite Divisibility ◽  
Random Sums ◽  
Generalized Gamma ◽  
Second Moments ◽  
Large Numbers ◽  
Generalized Negative Binomial Distribution

We investigate the proximity in terms of zeta-structured metrics of generalized negative binomial random sums to generalized gamma distribution with the corresponding parameters, extending thus the zeta-structured estimates of the rate of convergence in the Rényi theorem. In particular, we derive upper bounds for the Kantorovich and the Kolmogorov metrics in the law of large numbers for negative binomial random sums of i.i.d. random variables with nonzero first moments and finite second moments. Our method is based on the representation of the generalized negative binomial distribution with the shape and exponent power parameters no greater than one as a mixed geometric law and the infinite divisibility of the negative binomial distribution.

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