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首页> 外文期刊>Information Technology Journal >Textural Fabric Defect Detection using Adaptive Quantized Gray-level Co-occurrence Matrix and Support Vector Description Data
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Textural Fabric Defect Detection using Adaptive Quantized Gray-level Co-occurrence Matrix and Support Vector Description Data

机译:使用自适应量化灰度共现矩阵和支持向量描述数据的纹理织物缺陷检测

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摘要

A new defect detection algorithm base on Support Vector Data Description (SVDD) is proposed. A fabric texture model is built on the gray-level histogram of textural fabric image. Two Gray-level Co-occurrence Matrix (GLCM) features are used to characterize the fabric texture. And an adaptive quantization scheme base on the texture mode is proposed to reduce the size of GLCM and reduce the computational complexity of feature extraction. Besides, two new features are proposed to characterize the continuous property of the fabric defects. The SVDD classifier is used as a detector for defect detection. Experimental results of real fabric defects are provided to validate the effectiveness and robustness of the proposed detection algorithm. And a prototyped detection system is built to evaluate the real-time performance of the detection algorithm.
机译:提出了一种基于支持向量数据描述(SVDD)的缺陷检测算法。织物纹理模型建立在纹理织物图像的灰度直方图上。两个灰度共生矩阵(GLCM)功能用于表征织物纹理。提出了一种基于纹理模式的自适应量化方案,以减小GLCM的大小,降低特征提取的计算复杂度。此外,提出了两个新特征来表征织物缺陷的连续性质。 SVDD分类器用作缺陷检测的检测器。提供了真实织物缺陷的实验结果,以验证所提出的检测算法的有效性和鲁棒性。并建立了原型检测系统,以评估检测算法的实时性能。

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