Abstract
Selecting important features in classifying wood defects remains a challenging issue to the automated visual inspection domain. This study aims to address the extraction and analysis of features based on statistical texture on images of wood defects. A series of procedures including feature extraction using the Grey Level Dependence Matrix (GLDM) and feature analysis were executed in order to investigate the appropriate displacement and quantisation parameters that could significantly classify wood defects. Samples were taken from the KembangSemangkuk (KSK), Meranti and Merbau wood species. Findings from visual analysis and classification accuracy measures suggest that the feature set with the displacement parameter, d=2, and quantisation level, q=128, shows the highest classification accuracy. However, to achieve less computational cost, the feature set with quantisation level, q=32, shows acceptable performance in terms of classification accuracy.
Highlights
Wood is classified as a natural biological material and it is attacked by microorganisms [1]
The purpose of this study is to contribute to a part of the process in developing an automated vision inspection which is the feature extraction technique using the Grey Level Dependence Matrix (GLDM) for wood defect classification
RESEARCH METHOD This section explains in detail the research methodology for the proposed texture feature technique which is GLDM
Summary
Wood is classified as a natural biological material and it is attacked by microorganisms [1].These microorganisms can destroy the wood structure and allow the presence of defect on the wood surface, affecting the quality of the wood [2]. In order to avoid this from happening, the manufacturer should take preventive measure where they should check and determine the presence of defect on the wood surface. These wood defects can be det ect ed manually using visual inspection. The manual inspection cou ld lead to human error and this is affected depending on the skill level, experience of the worker and alertness. It takes a lot of time and the process is slow [3]. One of the solutions is to use a machine vision based inspection system which can save inspection time and indirectly the results can lead to a reliable quality control process [3]
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