Analysis Of Texture Features For Wood Defect Classification

Abdullah, Nur Dalila and Hashim, Ummi Rabaah and Ahmad, Sabrina and Salahuddin, Lizawati (2020) Analysis Of Texture Features For Wood Defect Classification. Bulletin Of Electrical Engineering And Informatics, 9 (1). pp. 121-128. ISSN 2302-9285

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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 Kembang Semangkuk (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

Item Type:	Article
Uncontrolled Keywords:	Automated Vision Inspection, Defect Detection, Feature Extraction, GLDM, Texture
Divisions:	Faculty of Information and Communication Technology > Department of Software Engineeering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	19 Jul 2021 15:48
Last Modified:	19 Jul 2021 15:48
URI:	http://eprints.utem.edu.my/id/eprint/25055
Statistic Details:	View Download Statistic

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