首页 | 本学科首页   官方微博 | 高级检索  
     

基于GLCM算法的图像纹理特征分析
引用本文:陈美龙,戴声奎.基于GLCM算法的图像纹理特征分析[J].通信技术,2012(2):108-111.
作者姓名:陈美龙  戴声奎
作者单位:华侨大学信息科学与工程学院,福建厦门361021
基金项目:华侨大学自然科学基金(批准号:09BS102) 华侨大学基本科研业务费专项基金(No.JB-ZR1145); 厦门市科技计划项目(No.3502Z20103026)
摘    要:深入研究灰度共生矩阵(GLCM,Gray Level Co-occurrence Matrix)算法,说明基于灰度共生矩阵的14个纹理特征具体意义,指出纹理特征之间存在冗余性。通过对纹理图像的灰度共生矩阵的计算分析和纹理特征提取实验,表明灰度共生矩阵能够反应图像的特点,与纹理特征描述图像的特点相对应,同时,图像的14个纹理特征之间存在一定程度的冗余,实际中可以根据图像纹理特征的差异,选择几个显著的纹理特征对图像进行分类。纹理特征分析和实验结果对图像纹理特征的应用具有普遍的指导意义。

关 键 词:灰度共生矩阵  纹理特征  图像分类

Analysis on Image Texture based on Gray-level Co-occurrence Matrix
CHEN Mei-long,DAI Sheng-kui.Analysis on Image Texture based on Gray-level Co-occurrence Matrix[J].Communications Technology,2012(2):108-111.
Authors:CHEN Mei-long  DAI Sheng-kui
Affiliation:(Institute of Information Science & Engineering,Huaqiao University,Xiamen Fujian 361021,China)
Abstract:In-depth study on gray level co-occurrence matrix(GLCM) is done,the meaning of the fourteen texture features based on gray level co-occurrence matrix is explained,and the redundancy existing between the texture features is pointed out.The analysis,calculation and extraction of texture image gray-level co-occurrence matrix indicate that gray-level co-occurrence matrix could be used to describe the image characteristics consistent with texture features,while certain redundancy exists between fourteen texture features of the image.In practice,several significant features are selected for the image texture classification based on the differences of the image texture features.Texture analysis and experimental results are of great guiding importance for the practical application of image texture features.
Keywords:gray-level co-occurrence matrix  texture feature  image classification
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号