Computer Vision-aided Atom Tracking in STEM Imaging

To address the SMC'17 data challenge -- "Data mining atomically resolved images for material properties", we first used the classic "blob detection" algorithms developed in computer vision to identify all atom centers in each STEM image frame. With the help of nearest neighb...

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Bibliographic Details
Main Authors Hui, Yawei, Liu, Yaohua
Format Journal Article
LanguageEnglish
Published 13.09.2018
Subjects
Computer Science - Computer Vision and Pattern Recognition
Online AccessGet full text
DOI10.48550/arxiv.1809.05076

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Summary:To address the SMC'17 data challenge -- "Data mining atomically resolved images for material properties", we first used the classic "blob detection" algorithms developed in computer vision to identify all atom centers in each STEM image frame. With the help of nearest neighbor analysis, we then found and labeled every atom center common to all the STEM frames and tracked their movements through the given time interval for both Molybdenum or Selenium atoms.
DOI:10.48550/arxiv.1809.05076