Automated knot detection with visual post-processing of Douglas-fir veneer images

C.L. Todoroki; Eini C. Lowell; Dennis Dykstra

doi:10.1016/j.compag.2009.10.002

Treesearch

Automated knot detection with visual post-processing of Douglas-fir veneer images

Formally Refereed

Authors:

C.L. Todoroki, Eini C. Lowell, Dennis Dykstra

Year:

2010

Type:

Scientific Journal

Station:

Pacific Northwest Research Station

DOI:

https://doi.org/10.1016/j.compag.2009.10.002

Source:

Computers and Electronics in Agriculture. 70: 163-171

Download (PDF 4.01 MB)

Download (PDF 4.01 MB): https://research.fs.usda.gov/download/treesearch/37235.pdf

Abstract

Knots 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.

Keywords

knot detection, image processing, veneer stiffness, acoustic velocity

Citation

Todoroki, 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.