Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Name
11661_2020_6008_ReferencePDF.pdf
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1.49 MB
Format
Adobe PDF
Checksum (MD5)
72ea922bfa8fd2d9f5a5592b5a58c570
Author(s)
Holm, Elizabeth A
Cohn, Ryan
Gao, Nan
Kitahara, Andrew R
Matson, Thomas P
Lei, Bo
Yarasi, Srujana R
Date Issued
September 29, 2020
Publisher
Springer US
Version
Author's final manuscript
Abstract
Abstract
Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s11661-020-06008-4
