Open AccessAnalysis of texture features for wood defect classification
Author(s) -
Nur Dalila Abdullah,
Ummi Rabaah Hashim,
Sabrina Ahmad,
Lizawati Salahuddin
Publication year - 2020
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v9i1.1553
Subject(s) - pattern recognition (psychology) , artificial intelligence , feature (linguistics) , feature extraction , texture (cosmology) , visual inspection , displacement (psychology) , set (abstract data type) , mathematics , computer science , image (mathematics) , psychology , philosophy , linguistics , psychotherapist , programming language
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.