12 October 2015 Using computer vision and compressed sensing for wood plate surface detection
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Abstract
Aiming at detecting the random and complicated characteristic of wood surface, we proposed a comprehensive detection algorithm based on computer vision and compressed sensing. First, integral projection method was used to trace and locate the position of a wood plate; then surface images were obtained by blocks. Second, multiscaled features were extracted from image to express the surface characteristic. Third, particle swarm optimization algorithm was used for multiscaled features selection. Finally, the defects and textures were detected by compressed sensing classifier. Five types of wood samples, including radial texture, tangential texture, wormhole, live knot, and dead knot, were used for tests, and the average classification accuracy was 94.7%.
© 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yizhuo Zhang, Sijia Liu, Wenjun Tu, Huiling Yu, Chao Li, "Using computer vision and compressed sensing for wood plate surface detection," Optical Engineering 54(10), 103102 (12 October 2015). https://doi.org/10.1117/1.OE.54.10.103102 . Submission:
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