Comparison between GLCM and modified Zernike moments for material surfaces identification from photo images
Fathin Liyana Zainudin, Abd Kadir Mahamad, Sharifah Binti Saon, Musli Nizam Yahya · 2014
Types of materials are one of an important data for research in acoustic engineering. This paper compares methods for extracting texture data of material surfaces for classification. Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments that is applied for image extraction are tested and compared with back propagation neural network used for classification. These methods are also applied to the Brodatz texture database as a general comparison. The GLCM method shows a good performance and regression, R>0.9 for the Brodatz database while the collected surfaces datasets using GLCM and modified Zernike moments as well as the Brodatz datasets using modified Zernike moments method had only managed an acceptable performance and regression of R>0.8.