Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
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...
| Main Authors: | , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131932 |
| _version_ | 1826217323151753216 |
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| author | Holm, Elizabeth A Cohn, Ryan Gao, Nan Kitahara, Andrew R Matson, Thomas P Lei, Bo Yarasi, Srujana R |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Holm, Elizabeth A Cohn, Ryan Gao, Nan Kitahara, Andrew R Matson, Thomas P Lei, Bo Yarasi, Srujana R |
| author_sort | Holm, Elizabeth A |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T17:02:08Z |
| format | Article |
| id | mit-1721.1/131932 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T17:02:08Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1319322023-01-20T21:32:06Z Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis Holm, Elizabeth A Cohn, Ryan Gao, Nan Kitahara, Andrew R Matson, Thomas P Lei, Bo Yarasi, Srujana R Massachusetts Institute of Technology. Department of Materials Science and Engineering 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. 2021-09-20T17:31:00Z 2021-09-20T17:31:00Z 2020-09-29 2020-11-13T04:31:51Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131932 en https://doi.org/10.1007/s11661-020-06008-4 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. The Minerals, Metals & Materials Society and ASM International application/pdf Springer US Springer US |
| spellingShingle | Holm, Elizabeth A Cohn, Ryan Gao, Nan Kitahara, Andrew R Matson, Thomas P Lei, Bo Yarasi, Srujana R Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| title | Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| title_full | Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| title_fullStr | Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| title_full_unstemmed | Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| title_short | Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| title_sort | overview computer vision and machine learning for microstructural characterization and analysis |
| url | https://hdl.handle.net/1721.1/131932 |
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