Asymmetric Generalized Gaussian DistributionParameters Estimation based on MaximumLikelihood, Moments and Entropy

Abstract: In this paper, a parametric histogram-based image segmentation methodis used where the gray level histogram is considered as a finite mixture of

Abstract In this paper, a parametric histogram-based image segmentation method is used where the gray level histogram is considered as a finite mixture of asymmetric

The obtained results are comparable to those of the maximum likelihood method which, however, manipulates high nonlinear equations (piece-wise function, log, etc.), contrarily to

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We address this lack through a comprehensive simulation study in R software using some of the best optimization algorithms (Newton, quasi-Newton and Nelder-Mead algorithms).. It

In the later paper Bernstein et al. [16] suggested applying the memoryless collision search method at the last step of the algorithm. The complexity of the memoryless collision

Considering the estimation issue of the MGGD parameters, the main contribution of this paper is to prove that the maximum likelihood estimator (MLE) of the scatter matrix exists and

For any shape parameter β ∈]0, 1[, we have proved that the maximum likelihood estimator of the scatter matrix exists and is unique up to a scalar factor.. Simulation results have