# Computer Vision-aided Atom Tracking in STEM Imaging

arXiv: https://arxiv.org/abs/1809.05076 · HTML: https://mlsanity.com/paper/1809.05076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders](https://mlsanity.com/paper/2412.16200/) (2024)

## Abstract (arXiv)

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.