Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix

Ying Chen; Feng Yu Yang

doi:10.4028/www.scientific.net/AMM.204-208.4746

Paper Titles

The Trajectory Planning of Robotic Operation Simulation Based on Virtual Reality
p.4729

A Tapering Method in the Construction of Covariance Regularization
p.4734

A CFD Simulation of Strouhal Number for U-Shaped Section Aqueducts
p.4738

Parameters Identification of Dual-Rate Hammerstein Systems Based on Finite Input Response Models
p.4742

Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix
p.4746

The Properties of Case-Weight Model when the Row and Column of Design Matrix Simultaneous Mitted
p.4751

Research on Characteristic Properties of Gray Level Co-Occurrence Matrix
p.4755

Prediction of Dynamic Deformation Monitoring Based on IGA Artificial Neural Network Model
p.4760

A Matching Theorem in FC-Spaces with the Application to Coincidence Questions
p.4766

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 204-208Analysis of Image Texture Features Based on Gray...

Analysis of Image Texture Features Based on Gray Level Co-Occurrence Matrix

Abstract:

Gray level co-occurrence matrix (GLCM) is a second-order statistical measure of image grayscale which reflects the comprehensive information of image grayscale in the direction, local neighborhood and magnitude of changes. Firstly, we analyze and reveal the generation process of gray level co-occurrence matrix from horizontal, vertical and principal and secondary diagonal directions. Secondly, we use Brodatz texture images as samples, and analyze the relationship between non-zero elements of gray level co-occurrence matrix in changes of both direction and distances of each pixels pair by. Finally, we explain its function of the analysis process of texture. This paper can provided certain referential significance in the application of using gray level co-occurrence matrix at quality evaluation of texture image.