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Nur Dalila Abdullah
Politeknik Muadzam Shah
Electrical & Electronics Engineering

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Analysis of texture features for wood defect classification Nur Dalila Abdullah; Ummi Raba'ah Hashim; Sabrina Ahmad; Lizawati Salahuddin
Bulletin of Electrical Engineering and Informatics Vol 9, No 1: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.861 KB) | DOI: 10.11591/eei.v9i1.1553

Abstract

Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the KembangSemangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accuracy.
Co-Authors Lizawati Salahuddin Sabrina Ahmad Ummi Raba'ah Hashim