# Overview: Computer vision and machine learning for microstructural characterization and analysis Source: [Overview: Computer vision and machine learning for microstructural characterization and analysis]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:54:19.823Z Authors: Elizabeth A. Holm, Ryan Cohn, Nan Gao, Andrew R. Kitahara, Thomas P. Matson, Bo Lei, Srujana Rao Yarasi Published: 2020-05-28T19:51:23.000Z ## Abstract The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()