applied computer vision methods for microstructure characterizationRecent 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. My research 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. Novel materials science applications 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.
A computer vision approach for automated analysis and classification of microstructural image dataIn Computational Materials Science (2015)
Straightforward application of the bag of visual words to microstructure images.
Characterizing powder materials using keypoint-based computer vision methodsIn Computational Materials Science (2016)
Exploring a synthetic powder materials dataset, created with Blender. Check out the Blender scripts and the dataset (writeup).
Computer vision and machine learning for autonomous characterization of AM powder feedstocksIn JOM (2017)
Exploring real powder micrographs.
Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructuresCurrently under review
Exploring ultrahigh carbon steel microstructures with SIFT, convolutional neural networks, and t-SNE.