@article{ecebe75a29bb4fe1b0976e73c0f603db,
title = "Quantitative multi-image analysis in metals research",
abstract = "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: [Figure not available: see fulltext.].",
author = "Demkowicz, {M. J.} and M. Liu and McCue, {I. D.} and M. Seita and J. Stuckner and K. Xie",
note = "Funding Information: MJD was supported by the US Department of Energy, National Nuclear Security Administration under award no. DE-NA0003857. IM was supported by an Early Career Faculty grant from NASA{\textquoteright}s Space Technology Research Grants Program. MS was supported by the Ministry of Education of Singapore, Official Number: MOE2017-T2-2-119. JS was supported by the NASA Transformational Tools and Technologies (T3) project under the Transformative Aeronautics Concept Program within the Aeronautics Research Mission Directorate. KX was supported by the US National Science Foundation, Division of Materials Research, under award no. 2004752. KX is grateful to W.S. Lin for assistance with DefectSegNet. Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = dec,
doi = "10.1557/s43579-022-00265-7",
language = "English (US)",
volume = "12",
pages = "1030--1036",
journal = "MRS Communications",
issn = "2159-6859",
publisher = "Cambridge University Press",
number = "6",
}