Kim Soo Chang, Kang Tae Jin
Intelligent Textile System Research Center and School of Materials Science and Engineering, Seoul National University, Seoul, Korea 151-744.
J Opt Soc Am A Opt Image Sci Vis. 2006 Nov;23(11):2690-701. doi: 10.1364/josaa.23.002690.
This paper proposes an approach for automated defect detection in homogeneous textiles using texture analysis. The texture features are extracted by the wavelet packet frame decomposition followed by the Karhunen-Loève transform. The texture feature vector for each pixel is used as an input to a Gaussian mixture model that determines whether or not each pixel is defective. The parameters of the Gaussian mixture model are estimated with nondefective textile images in supervised defect detection. An approach for unsupervised defect detection is also presented that can identify the heterogeneous subblocks on the basis of the Kullback-Leibler divergence between two Gaussian mixtures. The proposed method was evaluated on 25 different homogeneous textile image pairs, one of each pair with a defect and the other with no defect, and was compared with existing methods using texture analysis. The experimental results yielded visually good segmentation and an excellent detection rate with a low false alarm rate for both supervised and unsupervised defect detection. This confirms the validity of the proposed approach for automated defect detection and localization.
本文提出了一种利用纹理分析对均匀纺织品进行自动缺陷检测的方法。通过小波包框架分解提取纹理特征，然后进行卡尔胡宁-洛伊夫变换。每个像素的纹理特征向量被用作高斯混合模型的输入，该模型确定每个像素是否有缺陷。在有监督的缺陷检测中，利用无缺陷纺织品图像估计高斯混合模型的参数。还提出了一种无监督缺陷检测方法，该方法可以基于两个高斯混合之间的库尔贝克-莱布勒散度识别异质子块。该方法在25对不同的均匀纺织品图像上进行了评估，每对图像中的一幅有缺陷，另一幅无缺陷，并与现有的纹理分析方法进行了比较。实验结果在视觉上实现了良好的分割，并且在有监督和无监督缺陷检测中都具有优异的检测率和低误报率。这证实了所提自动缺陷检测和定位方法的有效性。
