Classification of wood defect images using local binary pattern variants

International Journal of Advances in Intelligent Informatics

View Publication Info
 
 
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
 
Contributor
 
Date 2020-03-29
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/392
10.26555/ijain.v6i1.392
 
Source International Journal of Advances in Intelligent Informatics; Vol 6, No 1 (2020): March 2020; 36-45
2548-3161
2442-6571
 
Language eng
 
Relation http://ijain.org/index.php/IJAIN/article/view/392/ijain_v6i1_p36-45
 
Rights https://creativecommons.org/licenses/by-sa/4.0
 

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