Wang Ning, Freysoldt Christoph, Zhang Siyuan, Liebscher Christian H, Neugebauer Jörg
Max-Planck-Institut für Eisenforschung GmbH, Max-Planck-Straße 1, 40237Düsseldorf, Germany.
Microsc Microanal. 2021 Sep 21:1-11. doi: 10.1017/S1431927621012770.
We present an unsupervised machine learning approach for segmentation of static and dynamic atomic-resolution microscopy data sets in the form of images and video sequences. In our approach, we first extract local features via symmetry operations. Subsequent dimension reduction and clustering analysis are performed in feature space to assign pattern labels to each pixel. Furthermore, we propose the stride and upsampling scheme as well as separability analysis to speed up the segmentation process of image sequences. We apply our approach to static atomic-resolution scanning transmission electron microscopy images and video sequences. Our code is released as a python module that can be used as a standalone program or as a plugin to other microscopy packages.
我们提出了一种无监督机器学习方法，用于对以图像和视频序列形式呈现的静态和动态原子分辨率显微镜数据集进行分割。在我们的方法中，我们首先通过对称操作提取局部特征。随后在特征空间中进行降维和聚类分析，为每个像素分配模式标签。此外，我们提出了步长和上采样方案以及可分离性分析，以加速图像序列的分割过程。我们将我们的方法应用于静态原子分辨率扫描透射电子显微镜图像和视频序列。我们的代码作为一个Python模块发布，可以用作独立程序或作为其他显微镜软件包的插件。
