CATALOG: a system for detection and rendering of internal log defects using computer tomography
Machine Vision and Applications
A System for Detection of Internal Log Defects by Computer Analysis of Axial CT Images
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Computers and Electronics in Agriculture
Modeling knot geometry in norway spruce from industrial CT images
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
A prototype vision system for analyzing CT imagery of hardwood logs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Knot segmentation in noisy 3d images of wood
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
Computers and Electronics in Agriculture
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An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a new approach for knot diameter measurements. The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images. A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and inclination as well). The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images. The results obtained are promising, with detection rates varying from 71% to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R^2 of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively.