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Proceedings Paper

Primary detection of hardwood log defects using laser surface scanning
Author(s): Edward Thomas; Liya Thomas; Lamine Mili; Roger W. Ehrich; A. Lynn Abbott; Clifford Shaffer
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Paper Abstract

The use of laser technology to scan hardwood log surfaces for defects holds great promise for improving processing efficiency and the value and volume of lumber produced. External and internal defect detection to optimize hardwood log and lumber processing is one of the top four technological needs in the nation’s hardwood industry. The location, type, and severity of defects on hardwood logs are the key indicators of log quality and value. These visual cues provide information about internal log characteristics and products for which the log is suitable. We scanned 162 logs with a high-resolution industrial four-head laser surface scanner. The resulting data sets contain hundreds of thousands of three-dimensional coordinate points. The size of the data and noise presented special problems during processing. Robust regression models were used to fit geometric shapes to the data. The estimated orthogonal distances between the fitted model and the log surface are converted to a two-dimensional image to facilitate defect detection. Using robust regression methods and standard image processing tools we have demonstrated that severe surface defects on hardwood logs can be detected using height and contour analyses of three-dimensional laser scan data.

Paper Details

Date Published: 22 May 2003
PDF: 11 pages
Proc. SPIE 5011, Machine Vision Applications in Industrial Inspection XI, (22 May 2003); doi: 10.1117/12.474036
Show Author Affiliations
Edward Thomas, U.S. Dept. of Agriculture Forest Service (United States)
Liya Thomas, Virginia Polytechnic Institute and State Univ. (United States)
Lamine Mili, Virginia Polytechnic Institute and State Univ. (United States)
Roger W. Ehrich, Virginia Polytechnic Institute and State Univ. (United States)
A. Lynn Abbott, Virginia Polytechnic Institute and State Univ. (United States)
Clifford Shaffer, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 5011:
Machine Vision Applications in Industrial Inspection XI
Martin A. Hunt; Jeffery R. Price, Editor(s)

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