Computer Vision-aided Atom Tracking in STEM Imaging
Yawei Hui, Yaohua Liu
TL;DR
This paper introduces a computer vision-based method for tracking atoms in STEM images, utilizing blob detection and nearest neighbor analysis to identify and follow atom movements over time, aiding material property analysis.
Contribution
It presents a novel application of classic computer vision techniques for atom tracking in STEM imaging, addressing the SMC'17 data challenge.
Findings
Successfully identified atom centers in STEM images
Tracked atom movements over time for Molybdenum and Selenium
Demonstrated effectiveness of blob detection and nearest neighbor analysis
Abstract
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.
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Taxonomy
TopicsMineral Processing and Grinding · Electron and X-Ray Spectroscopy Techniques · Genomics and Phylogenetic Studies