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Fabric Pilling Object Detection Based on Scale - Space Extremum

机译：基于尺度空间极值的织物起毛目标检测。

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摘要

In order to solve the problem of extraction of pilling features in objective assessment of fabric pilling grading, we propose a new method for detecting pilling object using scale-space extremum. In this paper, the pilling object is modeled as an anisotropic Gaussian kernel. Based on scale-space theory and derivation of isotropic Gaussian matched filter, an operator as polynomial combinations of Gaussian derivatives is used for automatic scale selection, which provided a close approximation to Gaussian matched filter. By scale-space extrema of the normalized operator filtering, the pilling object is located and its size is measured. Depending on the anisotropic Gaussian model parameters which estimated from local structure tensor matrix, the pilling object is finally segmented and recognized. The experimental results show that the proposed method is feasible for pilling object segmentation and recognition.
机译：为了解决织物起毛起球分级客观评价中起毛起球特征的提取问题，提出了一种用尺度空间极值法检测起毛起球的新方法。在本文中，起球对象被建模为各向异性高斯核。基于尺度空间理论和各向同性高斯匹配滤波器的推导，将算子作为高斯导数的多项式组合用于自动尺度选择，从而提供了与高斯匹配滤波器的近似值。通过归一化算子过滤的尺度空间极值，可以确定起球对象并测量其大小。根据从局部结构张量矩阵估计的各向异性高斯模型参数，最终对起球对象进行了分割和识别。实验结果表明，该方法对于起球目标的分割和识别是可行的。

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