Deep learning-based image segmentation for al-la alloy microscopic images

DOI number:10.3390/sym10040107

Journal:Symmetry

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.

Co-author:Xiaojuan Ban,Haiyou Huang,Yulian Chen,Wanbo Liu,Yonghong Zhi

First Author:Boyuan Ma

Indexed by:Journal paper

Discipline:Engineering

Document Type:J

Volume:10

Issue:107

Page Number:1-13

Translation or Not:no

Date of Publication:2018-01-01

Included Journals:SCI