Systematic feature analysis on timber defect images

Ummi Rabaah Hashim, Siti Zaiton Mohd Hashim, Azah Kamilah Muda, Kasturi Kanchymalay, Intan Ermahani Abd Jalil, Muhammad Hakim Othman


Feature extraction is unquestionably an important process in a pattern recognition system. A clearly 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 in order to construct an appropriate feature set that could significantly separate among defects and clear wood classes. The feature set is aimed for a later use in a timber defect detection system To assess the discrimination capability of the features extracted, visual exploratory analysis and statistical confirmatory 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 utilising the proposed set of texture features.


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

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