Quantitative multi-image analysis in metals research

Quantitative multi-image analysis (QMA) is the systematic extraction of new information and insight through the simultaneous analysis of multiple, related images. We present examples illustrating the potential for QMA to advance materials research in multi-image characterization, automatic feature i...

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Detalles Bibliográficos
Autores principales: Demkowicz, M. J., Liu, M., McCue, I. D., Seita, M., Stuckner, J., Xie, K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Computational Approaches for Materials Discovery and Development Prospective
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718709/
https://www.ncbi.nlm.nih.gov/pubmed/36474648
http://dx.doi.org/10.1557/s43579-022-00265-7
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author Demkowicz, M. J.
Liu, M.
McCue, I. D.
Seita, M.
Stuckner, J.
Xie, K.
author_facet Demkowicz, M. J.
Liu, M.
McCue, I. D.
Seita, M.
Stuckner, J.
Xie, K.
author_sort Demkowicz, M. J.
collection PubMed
description Quantitative multi-image analysis (QMA) is the systematic extraction of new information and insight through the simultaneous analysis of multiple, related images. We present examples illustrating the potential for QMA to advance materials research in multi-image characterization, automatic feature identification, and discovery of novel processing-structure–property relationships. We conclude by discussing opportunities and challenges for continued advancement of QMA, including instrumentation development, uncertainty quantification, and automatic parsing of literature data. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1557/s43579-022-00265-7.
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spelling pubmed-97187092022-12-04 Quantitative multi-image analysis in metals research Demkowicz, M. J. Liu, M. McCue, I. D. Seita, M. Stuckner, J. Xie, K. MRS Commun Computational Approaches for Materials Discovery and Development Prospective Quantitative multi-image analysis (QMA) is the systematic extraction of new information and insight through the simultaneous analysis of multiple, related images. We present examples illustrating the potential for QMA to advance materials research in multi-image characterization, automatic feature identification, and discovery of novel processing-structure–property relationships. We conclude by discussing opportunities and challenges for continued advancement of QMA, including instrumentation development, uncertainty quantification, and automatic parsing of literature data. GRAPHICAL ABSTRACT: [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1557/s43579-022-00265-7. Springer International Publishing 2022-10-14 2022 /pmc/articles/PMC9718709/ /pubmed/36474648 http://dx.doi.org/10.1557/s43579-022-00265-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Computational Approaches for Materials Discovery and Development Prospective
Demkowicz, M. J.
Liu, M.
McCue, I. D.
Seita, M.
Stuckner, J.
Xie, K.
Quantitative multi-image analysis in metals research
title Quantitative multi-image analysis in metals research
title_full Quantitative multi-image analysis in metals research
title_fullStr Quantitative multi-image analysis in metals research
title_full_unstemmed Quantitative multi-image analysis in metals research
title_short Quantitative multi-image analysis in metals research
title_sort quantitative multi-image analysis in metals research
topic Computational Approaches for Materials Discovery and Development Prospective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9718709/
https://www.ncbi.nlm.nih.gov/pubmed/36474648
http://dx.doi.org/10.1557/s43579-022-00265-7
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