Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
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 layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation.
Abstract
The characterization and analysis of microstructure is the foundation of\nmicrostructural science, connecting the materials structure to its composition,\nprocess history, and properties. Microstructural quantification traditionally\ninvolves a human deciding a priori what to measure and then devising a\npurpose-built method for doing so. However, recent advances in data science,\nincluding computer vision (CV) and machine learning (ML) offer new approaches\nto extracting information from microstructural images. This overview surveys CV\napproaches to numerically encode the visual information contained in a\nmicrostructural image, which then provides input to supervised or unsupervised\nML algorithms that find associations and trends in the high-dimensional image\nrepresentation. CV/ML systems for microstructural characterization and analysis\nspan the taxonomy of image analysis tasks, including image classification,\nsemantic segmentation, object detection, and instance segmentation. These tools\nenable new approaches to microstructural analysis, including the development of\nnew, rich visual metrics and the discovery of\nprocessing-microstructure-property relationships.\n