Asymmetric Generalized Gaussian Mixture Models and EM algorithm for Image Segmentation

We have improved the RJMCM method for model selection prediction of MGPs on two aspects: first, we extend the split and merge formula for dealing with multi-input regression, which

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

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

Histogram fitting and region uniformity measures on synthetic and real images reveal the effectiveness of the proposed model compared to the generalized Gaussian mixture model.

Key Words: — expectation maximization algorithm, fuzzy C-means algorithm, Gaussian mixture model, image segmentation, radiographic images,

Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm. Key words—