Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images
Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images
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通过一组饱和图像的流形学习和回归来有效估计透明微缺陷的形状
DOI：
10.3390/app10010385
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发表时间：
2020-01
期刊：
影响因子：
--
通讯作者：
Xiaopin Zhong
中科院分区：
文献类型：
--
作者：
Yuanlong Deng;Xizhou Pan;Xiaopin Zhong
In the industry of polymer film products such as polarizers, measuring the three-dimensional (3D) contour of the transparent microdefects, the most common defects, can crucially affect what further treatment should be taken. In this paper, we propose an efficient method for estimating the 3D shape of defects based on regression by converting the problem of direct measurement into an estimation problem using two-dimensional imaging. The basic idea involves acquiring structured-light saturated imaging data on transparent microdefects; integrating confocal microscopy measurement data to create a labeled data set, on which dimensionality reduction is performed; using support vector regression on a low-dimensional small-set space to establish the relationship between the saturated image and defects’ 3D attributes; and predicting the shape of new defect samples by applying the learned relationship to their saturated images. In the discriminant subspace, the manifold of saturated images can clearly show the changing attributes of defects’ 3D shape, such as depth and width. The experimental results show that the mean relative error (MRE) of the defect depth is 3.64% and the MRE of the defect width is 1.96%. The estimation time consumed in the Matlab platform is less than 0.01 s. Compared with precision measuring instruments such as confocal microscopes, our estimation method greatly improves the efficiency of quality control and meets the accuracy requirement of automated defect identification. It is therefore suitable for complete inspection of products.
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DOI：
10.1115/1.4041480
发表时间：
2018-10
期刊：
Journal of Manufacturing Science and Engineering
影响因子：
--
作者：
Zhu Ye;Wang Lianpo;Guo Yonggang;Bi Songlin;Zhai Chao;Baoyang Jiang;J. Ni
通讯作者：
Zhu Ye;Wang Lianpo;Guo Yonggang;Bi Songlin;Zhai Chao;Baoyang Jiang;J. Ni
影响因子：
10.4
作者：
Chia-Yen Lee;Tsung-Lun Tsai
通讯作者：
Chia-Yen Lee;Tsung-Lun Tsai
影响因子：
3.8
作者：
Zhenxing Hu;T. Xu;Huiyang Luo;R. Gan;Hongbing Lu
通讯作者：
Zhenxing Hu;T. Xu;Huiyang Luo;R. Gan;Hongbing Lu
影响因子：
4.2
作者：
Felice, Maria V.;Velichko, Alexander;Wilcox, Paul D.
通讯作者：
Wilcox, Paul D.
影响因子：
2.7
作者：
Mei, Shuang;Yang, Hua;Yin, Zhouping
通讯作者：
Yin, Zhouping