Study on the identification of the wood surface defects based on texture features

• Published in: Optik (Stuttgart) Vol. 126; no. 19; pp. 2231 - 2235
• Main Authors: YongHua, Xie, Jin-Cong, Wang
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.10.2015
• Subjects: Defects detection; GLCM; Tamura texture; Texture features; Wood; Defects detection; Wood; Tamura texture; Texture features; GLCM
• ISSN: 0030-4026; 1618-1336
• DOI: 10.1016/j.ijleo.2015.05.101

Wood surface defect detection technology is the intersect multidisciplinary technology between computer vision and pattern recognition, which has a high value and is widely used in the field of timber production and deep processing. This paper mainly takes three common defects such as dead knots, poles and living knots of wood for the study, it deeply researches on image segmentation and pattern recognition methods of wood. To ensure the reliability of the results of wood defect recognition, the selection of characteristic values is the crucial aspects of pattern recognition. Haipeng Yu extracted texture of the wood based on GLCM, Xuebing Bai also classified texture of the wood based on GLCM. In addition, researchers used wavelets, Markov random, fractal, local binary pattern and histogram to make some useful attempts with the study of wood texture feature extraction. The above study only applied a texture analysis method. As the diversity and complexity of the wood surface defect images, the success rate of using a certain type of feature detection method is still less than ideal from the application point of view. The study proposes a hybrid wood surface texture features based on defect detection method, which combines the integration of Tamura texture and GLCM method advantages of these two methods, so that the accuracy and robustness of the algorithm are effectively protected.