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Research on Detection of Fabric Defects Based on Singular Value Decomposition

机译:基于奇异值分解的织物疵点检测研究

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Singular value decomposition technique is widely employed in feature analysis due to its strong capability of feature expression. Aiming at detection of fabric defects, this paper gives an approach for the fabric defects extraction in an image based on the theories of singular value decomposition, and proposes the corresponding algorithm. Firstly, singular value decomposition is performed on sub-image of the entire image, size of a rectangle window so that the average of singular values of every sub-image is obtained. Then, according to last step the average of singular values of all of sub-image is calculated. Finally the fabric image is segmented by means of a threshold related to the average of singular values and the defects could be detected. By using singular value decomposition, the complexes of operation are reduced, and noise issues of the image may be overcome. Validity and feasibility of this approach is proved through several experiments of fabric defects detection.
机译:奇异值分解技术由于其强大的特征表达能力而被广泛应用于特征分析中。针对织物疵点的检测,本文基于奇异值分解理论提出了一种图像中的织物疵点提取方法,并提出了相应的算法。首先,对整个图像的子图像(矩形窗口的大小)执行奇异值分解,以便获得每个子图像的奇异值的平均值。然后,根据最后一步,计算所有子图像的奇异值的平均值。最后,通过与奇异值平均值相关的阈值对织物图像进行分割,从而可以检测出缺陷。通过使用奇异值分解,可以减少操作的复杂度,并且可以克服图像的噪声问题。通过多次织物缺陷检测实验证明了该方法的有效性和可行性。

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