Classification of wood defect images using local binary pattern variants

(1) * Rahillda Nadhirah Norizzaty Rahiddin Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(2) Ummi Rabaah Hashim Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(3) Nor Haslinda Ismail Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(4) Lizawati Salahuddin Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(5) Ngo Hea Choon Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(6) Siti Normi Zabri Mail (Centre for Telecommunication Research & Innovation, Universiti Teknikal Malaysia Melaka, Malaysia)
*corresponding author


This 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.


Automated visual inspection; Defect detection; Wood inspection; Wood defect detection; Local binary pattern



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