Systematic feature analysis on timber defect images

(1) * Ummi Rabaah Hashim Mail (Faculty of Information and Communications Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(2) Siti Zaiton Mohd Hashim Mail (Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor, Malaysia)
(3) Azah Kamilah Muda Mail (Computational Intelligence and Technologies Lab, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia)
(4) Kasturi Kanchymalay Mail (Computational Intelligence and Technologies Lab, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia)
(5) Intan Ermahani Abd Jalil Mail (Computational Intelligence and Technologies Lab, Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia)
(6) Muhammad Hakim Othman Mail (Faculty of Electronic and Computer Engineering, Universiti Teknikal Malaysia Melaka (UTeM), Melaka, Malaysia)
*corresponding author


Abstract


Feature extraction is unquestionably an important process in a pattern recognition system. A defined set of features makes the identification task more efficiently. This paper addresses the extraction and analysis of features based on statistical texture to characterize images of timber defects. A series of procedures including feature extraction and feature analysis was executed to construct an appropriate feature set that could significantly separate amongst defects and clear wood classes. The feature set aimed for later use in a timber defect detection system. For Accessing the discrimination capability of the features extracted, visual exploratory analysis and confirmatory statistical analysis were performed on defect and clear wood images of Meranti (Shorea spp.) timber species. Results from the analysis demonstrated that there was a significant distinction between defect classes and clear wood utilizing the proposed set of texture features.

Keywords


texture; feature extraction; timber surface; automated vision inspection; feature selection

   

DOI

https://doi.org/10.26555/ijain.v3i2.94
   

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