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| Automatic Defect Detection of Patterned Fabric via Combining the Optimal Gabor Filter and Golden Image Subtraction |
| Junfeng Jing, Shan Chen, Pengfei Li |
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Abstract A new algorithm based on optimal Gabor filter and the basic Golden
Image Subtraction (GIS) is presented for patterned fabric defect
detection. Firstly, the defect-free patterned fabric images are
processed to search optimal real Gabor filter parameters using
traditional Genetic Algorithm (GA). Then test patterned fabric
images are filtered according to the obtained optimal real Gabor
filter. Furthermore, the basic GIS are adopted to perform
subtractions between golden images from referenced fabric images and
test images to get resultant images. Finally, thresholding is
obtained by training a large amount of defect-free patterned fabric
samples to segment defects from fabric background. Experiment
results indicate that the average detection success rate is up to
96.31\% with ninety defective patterned images and ninety
defect-free patterned images. It demonstrates that the proposed
method is more efficient.
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| Cite this article: |
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Junfeng Jing,Shan Chen,Pengfei Li. Automatic Defect Detection of Patterned Fabric via Combining the Optimal Gabor Filter and Golden Image Subtraction[J]. Journal of Fiber Bioengineering and Informatics, 2015, 8(2): 229-239.
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2015, Vol. 8 
