Texture material classification based on T-GLCM and Tamura fusion features
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TP391

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    Abstract:

    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.

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CHEN Xu, GAO Yazhou, CHEN Shoujing, ZHU Dongliang. Texture material classification based on T-GLCM and Tamura fusion features[J]. Journal of Nanjing University of Information Science & Technology,2023,15(5):561-567

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  • Received:July 02,2021
  • Online: October 24,2023
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