Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis
复制标题
DOI：
10.1007/s11661-020-06008-4
复制
发表时间：
2020-09-29
影响因子：
2.8
通讯作者：
Yarasi, Srujana Rao
中科院分区：
文献类型：
--
作者：
Holm, Elizabeth A.;Cohn, Ryan;Yarasi, Srujana Rao
微结构表征和分析是微结构科学的基础，它将材料结构与成分、工艺历史和性能联系起来。微观结构量化传统上涉及到人类决定测量什么，然后设计一种方法来这样做。然而，计算机视觉(CV)和机器学习(ML)的最新进展为从微结构图像中提取信息提供了新的方法。本文概述了使用基于特征的表示或卷积神经网络(CNN)层对微结构图像中包含的视觉信息进行数值编码的CV方法，然后向发现高维图像表示中的关联和趋势的监督或非监督ML算法提供输入。用于微观结构表征和分析的CV/ML系统跨越图像分析任务的分类，包括图像分类、语义分割、对象检测和实例分割。这些工具实现了微观结构分析的新方法，包括开发新的、丰富的可视度量标准，以及发现加工-显微结构-性能关系。
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.