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DOI码：10.3390/sym10040107

发表刊物：Symmetry

摘要：Quantitative analysis through image processing is a key step to gain information regarding the microstructure of materials. In this paper, we develop a deep learning-based method to address the task of image segmentation for microscopic images using an Al–La alloy. Our work makes three key contributions. (1) We train a deep convolutional neural network based on DeepLab to achieve image segmentation and have significant results. (2) We adopt a local processing method based on symmetric overlap-tile strategy which makes it possible to analyze the microscopic images with high resolution. Additionally, it achieves seamless segmentation. (3) We apply symmetric rectification to enhance the accuracy of results with 3D information. Experimental results showed that our method outperforms existing segmentation methods.

合写作者：Xiaojuan Ban,Haiyou Huang,Yulian Chen,Wanbo Liu,Yonghong Zhi

第一作者：Boyuan Ma

论文类型：期刊论文

学科门类：工学

文献类型：J

卷号：10

期号：107

页面范围：1-13

是否译文：否

发表时间：2018-01-01

收录刊物：SCI