Comparison between GLCM and modified Zernike moments for material surfaces identification from photo images

Author

Zainudin, Fathin Liyana ; Mahamad, Abd Kadir ; Saon, Sharifah ; Yahya, Musli Nizam

Author_Institution

Fac. of Electr. & Electron. Eng., Univ. Tun Hussein Onn Malaysia, Parit Raja, Malaysia

fYear

2014

Firstpage

1

Lastpage

4

Abstract

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.

Keywords

acoustic materials; backpropagation; feature extraction; image classification; image texture; mechanical engineering computing; neural nets; regression analysis; surface acoustic waves; visual databases; Brodatz texture database; GLCM; acoustic engineering; back propagation neural network; classification; gray level cooccurrence matrix; image extraction; material surfaces identification; modified Zernike moments; photo images; regression; texture data extraction; Biological neural networks; Feature extraction; Materials; Surface texture; Surface treatment; Testing; GLCM; Zernike moments; back propagation Neural Network; texture analysis; texture classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Science and Technology (ICCST), 2014 International Conference on

Type

conf

DOI

10.1109/ICCST.2014.7045008

Filename

7045008