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Automated knot detection with visual post-processing of Douglas-fir veneer imagesAuthor(s): C.L. Todoroki; Eini C. Lowell; Dennis Dykstra
Source: Computers and Electronics in Agriculture. 70: 163-171
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
PDF: Download Publication (3.82 MB)
DescriptionKnots on digital images of 51 full veneer sheets, obtained from nine peeler blocks crosscut from two 35-foot (10.7 m) long logs and one 18-foot (5.5 m) log from a single Douglas-fir tree, were detected using a two-phase algorithm. The algorithm was developed using one image, the Development Sheet, refined on five other images, the Training Sheets, and then applied to all remaining sheets. In phase one, global thresholding was used to segment the image through a series of morphological operations to isolate regions likely to contain knots. In phase two, adaptive thresholding was applied to grey scale and red component segmented images to improve the accuracy of the segmented knot.
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CitationTodoroki, C.L.; Lowell, Eini C.; Dykstra, Dennis. 2010. Automated knot detection with visual post-processing of Douglas-fir veneer images. Computers and Electronics in Agriculture. 70: 163-171.
Keywordsknot detection, image processing, veneer stiffness, acoustic velocity
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