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The Design of Optimal Gabor Filter for Defect Detection in Fabric Image

机译：织物图像中最佳Gabor滤波器的设计

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摘要

The classification of defects is highly demanded for automated inspection of textile products. This paper addresses the raw textile defects detection problem using Optimal Gabor Filter. Gabor wavelet filter is chosen as a major technique to extract the texture features from textile fabrics. A novel feature selection method based on the statistical significant differences between (or among) classes such as analysis of variance (ANOVA) is utilized to design an optimal filter. Finally, proper thresholding ensure the segmentation of defect from the texture background. Experimental results are presented to demonstrate the efficiency of this method.
机译：缺陷的分类对于纺织产品的自动检查非常令人要求缺陷。本文使用最佳Gabor滤波器解决了原始纺织品缺陷检测问题。选用Gabor小波滤波器作为提取纺织面料纹理特征的主要技术。利用基于诸如方差分析（ANOVA）的类别（或间）之间的统计显着差异的新颖特征选择方法来设计最佳滤波器。最后，适当的阈值，确保从纹理背景中分割缺陷。提出了实验结果以证明这种方法的效率。

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