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Progress in analysis of computed tomography (CT) images of hardwood logs for defect detectionAuthor(s): Erol Sarigul; A. Lynn Abbott; Daniel L. Schmoldt
Source: Proceedings, ScanTech 2003, The Tenth International Conference on Scanning Technology and Process Optimization in the Wood Industry. 19-30.
Publication Series: Miscellaneous Publication
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DescriptionThis paper addresses the problem of automatically detecting internal defects in logs using computed tomography (CT) images. The overall purpose is to assist in breakdown optimization. Several studies have shown that the commercial value of resulting boards can be increased substantially if defect locations are known in advance, and if this information is used to make sawing decisions. The problem is difficult, particularly for hardwood species, because of the natural variations of wood density and because of the irregular placement of defects such as knots. In our previous work, we developed a processing approach that utilizes artificial neural networks (ANN) to classify CT log images on a pixel-by-pixel basis. The system uses small (e.g., 5-by-5) neighborhoods in an image make a preliminary classification decision, using labels such as “knot,” “split,” and “bark.” This approach has yielded high accuracy statistically, with classification rates often exceeding 95%. Subjectively, however, the results can often be improved somewhat through further processing steps. For example, relatively simple operations can be employed to remove small. spurious regions that are not statistically significant, although their removal significantly improves the appearance of the results. This paper presents recent results for two hardwood species (red oak and sugar maple), as well as preliminary results for a softwood species (black spruce).
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CitationSarigul, Erol; Abbott, A. Lynn; Schmoldt, Daniel L. 2003. Progress in analysis of computed tomography (CT) images of hardwood logs for defect detection. Proceedings, ScanTech 2003, The Tenth International Conference on Scanning Technology and Process Optimization in the Wood Industry. 19-30.
- Proceedings of the ninth Lake States Forest Tree Improvement Conference, August 22-23, 1969.
- An interactive machine-learning approach for defect detection in computed tomogaraphy (CT) images of hardwood logs
- Characterization of Defects in Lumber Using Color, Shape, and Density Information
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