Automatic Defect Detection of Patterned Fabric via Combining the Optimal Gabor Filter and Golden Image Subtraction (Journal of Fiber Bioengineering and Informatics, 2015)
A new algorithm based on optimal Gabor filter and the basic Golden Image Subtraction (GIS) is\rpresented for patterned fabric defect detection. Firstly, the defect-free patterned fabric images are\rprocessed to search optimal real Gabor filter parameters using traditional Genetic Algorithm (GA). Then\rtest patterned fabric images are filtered according to the obtained optimal real Gabor filter. Furthermore,\rthe basic GIS are adopted to perform subtractions between golden images from referenced fabric images\rand test images to get resultant images. Finally, thresholding is obtained by training a large amount\rof defect-free patterned fabric samples to segment defects from fabric background. Experiment results\rindicate that the average detection success rate is up to 96.31% with ninety defective patterned images\rand ninety defect-free patterned images. It demonstrates that the proposed method is more efficient.