Analysis Of Texture Features For Wood Defect Classification
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 extract...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute Of Advanced Engineering And Science (IAES)
2020
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| Online Access: | http://eprints.utem.edu.my/id/eprint/25055/2/1553-4229-1-PB.PDF http://eprints.utem.edu.my/id/eprint/25055/ https://beei.org/index.php/EEI/article/view/1553/1260 |
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| Summary: | 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 Kembang Semangkuk (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 |
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