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首页> 外文期刊>The Journal of the Textile Institute >Warp-knitted fabric defect segmentation based on non-subsampled Contourlet transform
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Warp-knitted fabric defect segmentation based on non-subsampled Contourlet transform

机译：基于非下采样Contourlet变换的经编织物疵点分割

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摘要

Differing from the traditional Contourlet transform, the non-subsampled Contourlet transform (NSCT) is proposed to apply in warp-knitted fabric defect segmentation. First, the Laplacian pyramid is used to achieve the decomposition of original fabric image. Second, the high frequency directional sub-band coefficients are extracted by means of the non-subsampled directional filter bank. Then, choose the best high frequency sub-band coefficient at every level based on regional energy maxima and reconstruct the image. Finally, the legible defect profile is obtained by adaptive threshold method and morphological processing. The experimental results including the common defects, such as broken warp, width barrier and oil, show that the NSCT could attain the correct segmentation on directional defect and regional defect. This method fits the directional changes of warp-knitted fabric defect. It provides a new way to detect warp-knitted fabric defects automatically.
机译：与传统的Contourlet变换不同，提出了非下采样Contourlet变换（NSCT）在经编织物疵点分割中的应用。首先，拉普拉斯金字塔用于实现原始织物图像的分解。其次，借助于非二次采样的定向滤波器组提取高频定向子带系数。然后，根据区域能量最大值在每个级别上选择最佳的高频子带系数并重建图像。最后，通过自适应阈值法和形态学处理获得清晰的缺陷轮廓。实验结果表明，NSCT可以很好地分割出方向性缺陷和区域性缺陷。该方法适合经编织物缺陷的方向变化。它提供了一种自动检测经编织物缺陷的新方法。

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