Computer Vision-aided Atom Tracking in STEM Imaging
This work addresses material science researchers needing automated atom tracking, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the SMC'17 data challenge by using computer vision blob detection and nearest neighbor analysis to track atom movements in STEM images, achieving identification and labeling of common atom centers across frames for Molybdenum and Selenium atoms.
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.