Microstructure Representations: Applied Computer Vision Methods for Microstructure Characterization
Recent advances in computing power and automated microstructural image acquisition have opened the doors to data-driven quantitative microstructure analysis. Extraction of salient microstructure features is a crucial enabling component in this rapidly developing field of research; in the past decade the computer vision community has made enormous progress in this area, much of which has gone relatively unexplored by the quantitative microstructure analysis community. This dissertation explores applications of image texture recognition algorithms to engineer efficiently computable generic microstructure descriptors, enabling quantitative microstructure comparisons between and across a wide variety of materials systems. The literature review serves as a broad, high-level introduction for the materials scientist to some of the major themes in image recognition, along with some brief discussion of their relationship to contemporary microstructure science. After establishing that these image texture recognition algorithms can be effectively applied to classify diverse microstructure datasets, I begin to explore novel materials science applications. These include characterization and qualification of powder materials, exploratory analysis of large microstructure datasets, and extraction of quantitative relationships between materials processing and properties metadata and microstructural image features. The fusion of microstructure image analysis and contemporary machine vision techniques will facilitate development of robust autonomous microscopy systems, and may support quantitative engineering standards for complex hierarchical microstructure systems.