Paper
31 December 2008 Wood surface texture inspection using automatic selection band for wavelet reconstruction
Chun-hai Hu, Hai-ping Liang
Author Affiliations +
Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 713038 (2008) https://doi.org/10.1117/12.819675
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China
Abstract
This paper is focused on the analysis of wood surface inspection to wood machining industries. A defect detection approach for texture image, which uses an efficient image restoration scheme in wavelet domain, is presented. First, the texture image is decomposed by using wavelet base function in terms of the optimum decomposition levels, and then the restoration image can be reconstructed by properly selecting the smooth subimage or the detail subimages at best resolution levels. The homogeneous texture pattern can be effectively removed and only local defects are preserved in the restored image. A subband selection procedure is developed to automatically determine the best reconstruction parameters based on the energy distribution of wavelet coefficients. Then binarized image is received after image segmentation, At last the methods of image post-processing mathematical morphology were used in segmentation image. Experiments demonstrate the validity of the method, and show the potential possibility of real-time processing in an on-line wood surface inspection.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chun-hai Hu and Hai-ping Liang "Wood surface texture inspection using automatic selection band for wavelet reconstruction", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 713038 (31 December 2008); https://doi.org/10.1117/12.819675
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CITATIONS
Cited by 3 scholarly publications.
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KEYWORDS
Wavelets

Image segmentation

Image restoration

Inspection

Image filtering

Defect detection

Image resolution

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