Wood defect identification using convolutional neural network features with support vector machine classifier
Accurate classification of wood surface defects is essential for maintaining product quality and minimizing material waste in the timber industry. However, achieving high classification accuracy is challenging due to the limited availability of labeled datasets, particularly across diverse wood spec...
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| Format: | Thesis |
| Language: | en en |
| Published: |
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29384/1/Wood%20defect%20identification%20using%20convolutional%20neural%20network%20features%20with%20support%20vector%20machine%20classifier%20%2824%20pages%29.pdf http://eprints.utem.edu.my/id/eprint/29384/2/Wood%20defect%20identification%20using%20convolutional%20neural%20network%20features%20with%20support%20vector%20machine%20classifier.pdf http://eprints.utem.edu.my/id/eprint/29384/ |
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