Abstract
Computer vision methods can benefit wood processing industry. We propose a method to detect wood surface quality and classify wood samples into sound and defective classes. Gray level histogram statistical features and gray level co-occurrence matrix (GLCM) texture features are extracted from wood surface images and combined for classification. A half circle template is proposed to generate GLCM, avoiding calculating distances at each pixel every time and speeding up the algorithm greatly. The proposed approach uses more pixel information than traditional four-angle method, resulting in a significantly higher classification accuracy. Moreover the running time demonstrates our algorithm is efficient and suitable for real-time applications.
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© 2015 Springer International Publishing Switzerland
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Wang, D., Liu, Z., Cong, F. (2015). Wood Surface Quality Detection and Classification Using Gray Level and Texture Features. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_28
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DOI: https://doi.org/10.1007/978-3-319-25393-0_28
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