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Optical Engineering

Using computer vision and compressed sensing for wood plate surface detection
Author(s): Yizhuo Zhang; Sijia Liu; Wenjun Tu; Huiling Yu; Chao Li
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Paper 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%.

Paper Details

Date Published: 12 October 2015
PDF: 10 pages
Opt. Eng. 54(10) 103102 doi: 10.1117/1.OE.54.10.103102
Published in: Optical Engineering Volume 54, Issue 10
Show Author Affiliations
Yizhuo Zhang, Northeast Forestry Univ. (China)
Sijia Liu, Northeast Forestry Univ. (China)
Wenjun Tu, Northeast Forestry Univ. (China)
Huiling Yu, Northeast Forestry Univ. (China)
Chao Li, Northeast Forestry Univ. (China)

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