Classification Of Wood Defect Images Using Local Binary Pattern Variants

Rahiddin, Rahillda Nadhirah Norizzaty and Hashim, Ummi Rabaah and Ismail, Nor Haslinda and Salahuddin, Lizawati and Ngo, Hea Choon and Zabri@Suhaimi, Siti Normi (2020) Classification Of Wood Defect Images Using Local Binary Pattern Variants. International Journal of Advances in Intelligent Informatics, 6 (1). pp. 36-45. ISSN 2442-6571

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Automated visual inspection, Defect detection, Local binary pattern, Wood defect detection, Wood inspection
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	20 Apr 2021 11:48
Last Modified:	20 Apr 2021 11:48
URI:	http://eprints.utem.edu.my/id/eprint/25004
Statistic Details:	View Download Statistic

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