Performance of an Objective Fabric Pilling Evaluation Method
Performance of an Objective Fabric Pilling Evaluation Method
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DOI：
10.1177/0040517510361802
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发表时间：
2010-03
影响因子：
2.3
通讯作者：
Junmin Zhang;Xungai Wang;S. Palmer
中科院分区：
文献类型：
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作者：
Junmin Zhang;Xungai Wang;S. Palmer
在前期工作中，我们建立了基于二维双树复小波变换（2DDTCWT）图像重建和神经网络非线性分类的织物起毛起球客观评价原理。该原理证明工作使用标准起球测试图像进行。在这里，我们展示了实际操作的客观起球评价方法，使用大量的真实的织物起球样品。我们表明，打桩分类结果从一个训练有素的多层感知器神经网络实现了回归相关性约96%，与相应的人类专家起球评级。
In previous work, we established the principle of objective fabric pilling evaluation based on two-dimensional dual-tree complex wavelet transform (2DDTCWT) image reconstruction and non-linear classification using a neural network. This proof-of-principle work was performed using standard pilling test images. Here, we demonstrate the practical operation of the objective pilling evaluation method using a large set of real fabric pilling samples. We show that piling classification results from a trained multiple-layer perceptron neural network achieve a regression correlation of approximately 96% with the corresponding human expert pilling ratings.