Local texture representation for timber defect recognition based on variation of LBP

Rahiddin, Rahillda Nadhirah Norizzaty and Hashim, Ummi Rabaah and Salahuddin, Lizawati and Kanchymalay, Kasturi and Wibawa, Aji Prasetya and Teo, Hong Chun (2022) Local texture representation for timber defect recognition based on variation of LBP. International Journal of Advanced Computer Science and Applications, 13 (10). pp. 443-448. ISSN 2158-107X

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Abstract

This paper evaluates timber defect classification performance across four various Local Binary Patterns (LBP). The light and heavy timber used in the study are Rubberwood, KSK, Merbau, and Meranti, and eight natural timber defects involved; bark pocket, blue stain, borer holes, brown stain, knot, rot, split, and wane. A series of LBP feature sets were created by employing the Basic LBP, Rotation Invariant LBP, Uniform LBP, and Rotation Invariant Uniform LBP in a phase of feature extraction procedures. Several common classifiers were used to further separate the timber defect classes, which are Artificial Neural Network (ANN), J48 Decision Tree (J48), and K-Nearest Neighbor (KNN). Uniform LBP with ANN classifier provides the best performance at 63.4%, superior to all other LBP types. Features from Merbau provide the greatest F-measure when comparing the performance of the ANN classifier with Uniform LBP across timber fault classes and clean wood, surpassing other feature sets.

Item Type:	Article
Uncontrolled Keywords:	Automated visual inspection, Local binary pattern, Timber defect classification, Texture feature, Feature extraction
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Sabariah Ismail
Date Deposited:	28 Mar 2023 14:15
Last Modified:	28 Mar 2023 14:15
URI:	http://eprints.utem.edu.my/id/eprint/26382
Statistic Details:	View Download Statistic

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