An Automated and Unbiased Grain Segmentation Method based on Directional Reflectance Microscopy, Wittwer et al.

This repository contains the data and code necessary to reproduce the results presented in our publication.

Abstract:

Identifying individual grains from sectioned polycrystalline metals is a foundational task of microstructure analysis. However, traditional grain segmentation methods applied to optical micrographs may suffer from the lack of optical contrast between grains and require the manual selection of adjustable parameters to achieve acceptable segmentation results. We propose an alternative method which takes advantage of a multi-angle optical microscopy technique termed directional reflectance microscopy. By combining dimensionality reduction, similar-dissimilar classification, and multi-region merging of surface directional reflectance, our method enables fully automated and reliable grain segmentation of polycrystalline surfaces. We apply our method to metal samples with different crystal structures and grain orientation distributions. Our results suggest applicability of the method to a wide range of microstructures, enabling a more objective, robust, and universal characterization of polycrystalline metals.

本仓库包含复现我们出版物中呈现结果的所需数据和代码摘要：从分割的多晶金属样品中识别单个晶粒是微观结构分析的基础任务。然而，传统应用于光学显微照片的晶粒分割方法可能因晶粒间光学对比度不足而受到影响，且需要手动选择可调参数以达到可接受的分割结果。我们提出了一种替代方法，该方法利用了一种称为定向反射显微镜的多角度光学显微镜技术。通过结合降维、相似-差异分类以及表面定向反射的多区域合并，我们的方法能够实现多晶表面的全自动和可靠的晶粒分割。我们将该方法应用于具有不同晶体结构和晶粒取向分布的金属样品。我们的结果表明，该方法适用于广泛的微观结构，使得对多晶金属的表征更加客观、稳健和通用。