(Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)(2) Ummi Rabaah Hashim
(Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)(3) Nor Haslinda Ismail
(Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)(4) Lizawati Salahuddin
(Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)(5) Ngo Hea Choon
(Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)(6) Siti Normi Zabri
(Centre for Telecommunication Research & Innovation, Universiti Teknikal Malaysia Melaka, Malaysia)*corresponding author
AbstractThis paper presents an analysis of the statistical texture representation of the Local Binary Pattern (LBP) variants in the classification of wood defect images. The basic and variants of the LBP feature set that was constructed from a stage of feature extraction processes with the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP. For significantly discriminating, the wood defect classes were further evaluated with the use of different classifiers. By comparing the results of the classification performances that had been conducted across the multiple wood species, the Uniform LBP was found to have demonstrated the highest accuracy level in the classification of the wood defects.
KeywordsAutomated visual inspection; Defect detection; Wood inspection; Wood defect detection; Local binary pattern
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DOIhttps://doi.org/10.26555/ijain.v6i1.392 |
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