基于T-GLCM和Tamura融合特征的纹理材质分类

虚拟现实力触觉再现对于图像纹理特征提取的要求越来越高，纹理因素复杂且无规律，单一的纹理提取算法并不能准确地描述图像纹理的特点.因此提出基于GLCM(灰度共生矩阵)和Tamura融合特征的纹理材质分类算法.此外，本文对传统灰度共生矩阵GLCM进行优化，提出了改进的GLCM(T-GLCM)算子，提升了GLCM的旋转不变性并减少了大量的冗余信息.利用Tamura纹理特征对图像进行量化，然后将各特征区域量化后级联成一组特征向量，融合T-GLCM的纹理特征，通过支持向量机(SVM)对纹理材质进行分类.实验结果表明，相比传统纹理特征提取算法，本文算法具有更高的分类精度且鲁棒性更好.

Texture material classification based on T-GLCM and Tamura fusion features

Virtual reality haptic rendering has high requirements for image texture feature extraction.However, a single texture extraction algorithm cannot accurately describe the characteristics of image texture due to the complex and irregular texture factors.Therefore, a texture material classification approach based on GLCM (Gray-Level Co-occurrence Matrix) and Tamura fusion features is proposed.Additionally, we optimize the GLCM and propose the T-GLCM operator, thus improve the rotation invariance of GLCM pair and reduce a lot of redundant information.In this approach, the Tamura texture features are used to quantify the image, and the feature regions are quantified and then cascaded into a set of feature vectors.The texture features of T-GLCM are fused, and the texture materials are classified by Support Vector Machine (SVM).The experimental results show that the proposed approach outperforms traditional texture feature extraction algorithms in classification accuracy and robustness.