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首页> 外文期刊>The Journal of the Textile Institute >Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method
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Fabric defect detection based on multi-scale wavelet transform and Gaussian mixture model method

机译：基于多尺度小波变换和高斯混合模型的织物疵点检测

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摘要

This paper proposed an approach, which is based on multi-scale wavelet transform and Gaussian mixture model, to solve the problem about automated fabric defect detection and improve the quality of fabric in the production. Firstly, the sample image was tackled by the "Pyramid" wavelet decomposition algorithm, and the new images were obtained by reconstructing with the produced wavelet coefficients using wavelet thresholding denoising method. Secondly, the obtained new images were segmented by applying the Gaussian mixture model that was based on the Expectation-Maximization (EM) algorithm. Various fabric samples were used in the evaluation, and the experimental results showed that the designed algorithm could precisely locate the position of defect and segment the defect.
机译：提出了一种基于多尺度小波变换和高斯混合模型的方法，解决了织物疵点自动检测的问题，提高了织物的生产质量。首先，通过“金字塔”小波分解算法处理样本图像，并通过使用小波阈值去噪方法用产生的小波系数重构来获得新图像。其次，通过应用基于期望最大化（EM）算法的高斯混合模型对获得的新图像进行分割。评价中使用了各种织物样品，实验结果表明，所设计的算法能够准确定位缺陷位置并进行缺陷分割。

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