We propose a learning-based approach for automatic detection of fabric defects. Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT). The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics. In addition to being compact and fast to compute, these signatures enable accurate localization of defects. Our defect detection system is based on three main steps. In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation. In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics. Finally, defects are detected during image inspection by testing local patches using the learned BC. Our approach can deal with multiple types of textile fabrics, from simple to more complex ones. Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods.

中文翻译：

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在 Contourlet 域中使用基于学习的局部纹理分布进行自动织物缺陷检测


我们提出了一种基于学习的方法来自动检测织物缺陷。我们的方法基于使用冗余轮廓波变换（RCT）对织物图案进行统计表示。 RCT 系数的分布使用广义高斯 (MoGG) 的有限混合进行建模，它构成了区分有缺陷和无缺陷织物的统计特征。除了紧凑且计算速度快之外，这些签名还可以准确定位缺陷。我们的缺陷检测系统基于三个主要步骤。第一步，应用预处理来检测图像分解和签名计算的基本图案尺寸。第二步，使用标记的织物样本来训练贝叶斯分类器 (BC)，以区分无缺陷和有缺陷的织物。最后，通过使用学习到的 BC 测试局部补丁，在图像检查期间检测缺陷。我们的方法可以处理多种类型的纺织面料，从简单的到更复杂的。 TILDA 数据库上的实验表明，与最近最先进的方法相比，我们的方法产生了更好的结果。