Defect identification and statistics toolbox: automated defect analysis for scanning probe microscopy images
Defect identification and statistics toolbox: automated defect analysis for scanning probe microscopy images
复制标题
缺陷识别和统计工具箱：扫描探针显微镜图像的自动缺陷分析
DOI：
10.1088/1361-648x/abc1b2
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Hollen, Shawna M
中科院分区：
文献类型：
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作者：
Gudinas, Alana;Moscatello, Jason;Hollen, Shawna M
对扫描探针显微镜 (SPM) 图像中的缺陷进行识别和分类是一项重要的任务，但手动执行起来非常繁琐。在本文中，我们提出了缺陷识别和统计工具箱（DIST），这是一个用于识别和分析 SPM 图像中原子缺陷的图像处理工具箱。 DIST 将自动化与用户输入相结合，以准确有效地识别缺陷并自动计算关键统计数据。我们描述了使用 DIST 进行交互式图像处理、生成用于从图像背景中分离极值的等值线图以及识别缺陷的过程。
Identifying and classifying defects in scanning probe microscopy (SPM) images is an important task that is tedious to perform by hand. In this paper we present the defect identification and statistics toolbox (DIST), an image processing toolbox for identifying and analyzing atomic defects in SPM images. DIST combines automation with user input to accurately and efficiently identify defects and automatically compute critical statistics. We describe using DIST for interactive image processing, generating contour plots for isolating extrema from an image background, and processes for identifying defects.