Asymmetric Generalized Gaussian Mixture Models and EM Algorithm for Image Segmentation

Authors:
Nafaa Nacereddine;Salavatore Tabbone;Djemel Ziou;Latifa Hamami
Affiliations:
-;-;-;-
Venue:
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Year:
2010

Citing 0
Cited 2

Finite asymmetric generalized Gaussian mixture models learning for infrared object detection

Computer Vision and Image Understanding
A generalized multiclass histogram thresholding approach based on mixture modelling

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a parametric and unsupervised histogram-based image segmentation method is presented. The histogram is assumed to be a mixture of asymmetric generalized Gaussian distributions. The mixture parameters are estimated by using the Expectation Maximization algorithm. 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.