Classification of wood defect images using local binary pattern variants

International Journal of Advances in Intelligent Informatics

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Field Value
Title Classification of wood defect images using local binary pattern variants
Creator Rahiddin, Rahillda Nadhirah Norizzaty
Hashim, Ummi Rabaah
Ismail, Nor Haslinda
Salahuddin, Lizawati
Choon, Ngo Hea
Zabri, Siti Normi
Subject Automated visual inspection; Defect detection; Wood inspection; Wood defect detection; Local binary pattern
Description 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.
Publisher Universitas Ahmad Dahlan
Date 2020-03-29
Type info:eu-repo/semantics/article

Format application/pdf
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 1 (2020): March 2020; 36-45
Language eng

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