Texture Image Segmentation Based on Gaussian Mixture Models and Gray Level Co-occurrence Matrix

2010 ◽  
Author(s):  
Jian Yu
Keyword(s):  
Image Segmentation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Gray Level ◽  
Texture Image ◽  
Texture Image Segmentation ◽  
Occurrence Matrix

Texture Image Segmentation Based on GLCM

2012 ◽  
Vol 220-223 ◽  
pp. 1398-1401 ◽  
Author(s):  
Min Li ◽  
Jian Jun Liao
Keyword(s):  
Image Segmentation ◽  
Curve Fitting ◽  
Gray Level ◽  
Texture Image ◽  
Object Contour ◽  
Fitting Method ◽  
Texture Image Segmentation ◽  
The Matrix ◽  
Curve Fitting Method ◽  
Occurrence Matrix

The paper proposed a method on marble texture image segmentation based on Gray Level Co-occurrence Matrix (GLCM). At first, compute the Contrast matrix on basis of GLCM. Then choose the maximum of the matrix as the threshold to segment the object. At last extract the object contour with curve fitting method. Experiment results show that the method is accuracy.

Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

2011 ◽  
Vol 474-476 ◽  
pp. 442-447
Author(s):  
Zhi Gao Zeng ◽  
Li Xin Ding ◽  
Sheng Qiu Yi ◽  
San You Zeng ◽  
Zi Hua Qiu
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Mixture Models ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Image Data ◽  
Gaussian Mixture ◽  
Parameters Estimation ◽  
Video Image

In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

Fast estimation of Gaussian mixture models for image segmentation

2011 ◽  
Vol 23 (4) ◽  
pp. 773-789 ◽  
Author(s):  
Nicola Greggio ◽  
Alexandre Bernardino ◽  
Cecilia Laschi ◽  
Paolo Dario ◽  
José Santos-Victor
Keyword(s):  
Image Segmentation ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Fast Estimation
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