Symmetry 2018, 10(4), 107; doi:10.3390/sym10040107
Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images
Boyuan Ma 1,2, Xiaojuan Ban 1,2,*
, Haiyou Huang 3,*
, Yulian Chen 1,2, Wanbo Liu 3 and Yonghong Zhi 4
, Yulian Chen 1,2, Wanbo Liu 3 and Yonghong Zhi 41
School of Computer and Communication Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
2
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Xueyuan Road 30, Haidian District, Beijing 100083, China
3
Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China
4
Mechanical and Electrical Design and Research Institute of Shanxi Province, Shengli Street 228, Xinghualing District, Taiyuan 030009, Shanxi, China
*
Authors to whom correspondence should be addressed.
Received: 24 March 2018 / Revised: 2 April 2018 / Accepted: 3 April 2018 / Published: 13 April 2018
(This article belongs to the Special Issue Symmetry in Cooperative Applications III)
Abstract
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. View Full-TextKeywords:
deep convolution neural network; image segmentation; microscopic images of Al-La alloy; overlap-tile strategy; 3D information
▼
Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Share & Cite This Article
MDPI and ACS Style
Ma, B.; Ban, X.; Huang, H.; Chen, Y.; Liu, W.; Zhi, Y. Deep Learning-Based Image Segmentation for Al-La Alloy Microscopic Images. Symmetry 2018, 10, 107.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
Related Articles
Article Metrics
Comments
[Return to top]
Symmetry
EISSN 2073-8994
Published by MDPI AG, Basel, Switzerland
RSS
E-Mail Table of Contents Alert