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首页> 外文期刊>Fibres & textiles in Eastern Europe >Warp-knitted Fabric Defect Segmentation Based on the Shearlet Transform
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Warp-knitted Fabric Defect Segmentation Based on the Shearlet Transform

机译：基于Shearlet变换的经编织物疵点分割

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The Shearlet transform has been a burgeoning method applied in the area of image processing recently which, differing from the Wavelet transform, has excellent properties in processing singularities for multidimensional signals. Not only is it similar to the performance of the Curvelet transform, it also overcomes the disadvantage of the Curvelet transform with respect to discretization. In this paper, the Shearlet transform with segmented threshold de-nosing is proposed to segment a warp-knitted fabric defect. Firstly a warp-knitted fabric image of size 512*512 is filtered by the Laplacian Pyramid transform and decomposed into low frequency and high frequency coefficients. Secondly the high frequency coefficients are operated with a pseudo-polar grid and then convoluted by the window function. Thirdly the shearlet coefficients will be obtained through redefining the Cartesian coordinates from the pseudo-polar grid coordinates and de-noised by the segmented threshold method. Then the coefficients which have high energy are selected for reconstruction in an inverse way using the previous steps. Finally the iterative threshold method and object operation based on morphology are applied to segment out the defect profile. The experiment's result states that the Shearlet transform shows excellent performance in segmenting a common warp-knitted fabric defect, indicating that the segment results can be applied for further defect automatic recognition.
机译：Shearlet变换是最近在图像处理领域应用的一种新兴方法，与Wavelet变换不同，Shearlet变换在处理多维信号的奇点时具有出色的性能。它不仅与Curvelet变换的性能相似，而且还克服了Curvelet变换在离散化方面的缺点。本文提出了采用分段阈值降噪的Shearlet变换来分割经编织物的缺陷。首先，通过拉普拉斯金字塔变换对尺寸为512 * 512的经编织物图像进行滤波，并将其分解为低频和高频系数。其次，高频系数通过伪极性网格进行运算，然后通过窗函数进行卷积。第三，通过从伪极网格坐标中重新定义笛卡尔坐标并通过分段阈值方法对噪声进行去噪，可以获得小波系数。然后，使用先前的步骤以相反的方式选择具有高能量的系数以进行重构。最后应用迭代阈值法和基于形态学的目标运算来分割缺陷轮廓。实验结果表明，Shearlet变换在分割常见的经编织物缺陷方面显示出卓越的性能，表明该结果可用于进一步的缺陷自动识别。

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