Evaluation of texture feature based on basic local binary pattern for wood defect classification

(1) * Eihab Abdelkariem Bashir Ibrahim Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(2) Ummi Raba'ah Hashim Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(3) Lizawati Salahuddin Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(4) Nor Haslinda Ismail 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) Kasturi Kanchymalay Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(7) Siti Normi Zabri Mail (Centre for Telecommunication Research & Innovation, Universiti Teknikal Malaysia Melaka, Malaysia)
*corresponding author


Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.




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