|Abstract or Summary
- Machine vision is widely used in scientific areas and non-wood using
industries, but the extreme variability of wood has limited its adoption by forest
products industries. However, it is now becoming a key factor in further automation
of the forest products industry. As a very important part of machine vision,
developing image segmentation algorithms that can be used for wood products is an
ambitious undertaking. The focus of this research was to adapt existing and develop
some new segmentation algorithms which could be used to detect defects on veneer
Nine algorithms covering three segmentation technique categories were
explored. Three existing edge detection algorithms were modified for use on veneer
images, and four existing thresholding algorithms were adapted in both global and
local versions. Two new region extraction algorithms were developed specifically for
defect detection on veneer surfaces.
The performances of these nine algorithms were tested and compared under the
combinations of two camera resolutions (5-bit and 8-bit), three color spaces (RGB,
Lab, and gray-scale), and seven surface features (clear wood, blue stain, loose knot,
pitch pocket, pitch streak, tight knot, and wane). Ten sample images for each of
seven surface features on Douglas-fir veneer [Pseudotsuga menziesii] were used. Ten
measures were proposed for performance evaluation. A multi-factor factorial
ANOVA was used in the performance tests and comparisons.
The best combinations of camera resolution and color space for each of the
algorithms were determined. The 5-bit and 8-bit camera resolutions were not
significantly different for the three edge detection and two region extraction
algorithms, but the 8-bit camera resolution was better for all but one of the
thresholding algorithms. That exception was the global Otsu thresholding algorithm,
for which the 5-bit camera resolution was better. The RGB color space was the best
for all algorithms. Overall, the two region extraction algorithms were the best.
Under the best combination of factors, those two algorithms provided the highest
defect detection accuracies of 91% for pitch streak samples and over 95% for loose
knot, tight knot, and pitch pocket samples. These results were accomplished while
still providing clear wood accuracies of over 95%. The one performance exception
was blue stain, for which no satisfactory algorithm was found.