# Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation

arXiv: https://arxiv.org/abs/2302.00816 · HTML: https://mlsanity.com/paper/2302.00816/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 72 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 4 of 4)

- [Edge, Ridge, and Blob Detection with Symmetric Molecules](https://mlsanity.com/paper/1901.09723/) (2019)
- [A Novel Ridge Detector for Nonstationary Multicomponent Signals: Development and Application to Robust Mode Retrieval](https://mlsanity.com/paper/2009.13123/) (2020)
- [Feature detection and hypothesis testing for extremely noisy nanoparticle images using topological data analysis](https://mlsanity.com/paper/2209.13584/) (2022)
- [Development of an Efficient and Flexible Pipeline for Lagrangian Coherent Structure Computation](https://mlsanity.com/paper/2209.13775/) (2022)

## Abstract (arXiv)

Ridge detection is a classical tool to extract curvilinear features in image processing. As such, it has great promise in applications to material science problems; specifically, for trend filtering relatively stable atom-shaped objects in image sequences, such as Transmission Electron Microscopy (TEM) videos. Standard analysis of TEM videos is limited to frame-by-frame object recognition. We instead harness temporal correlation across frames through simultaneous analysis of long image sequences, specified as a spatio-temporal image tensor. We define new ridge detection algorithms to non-parametrically estimate explicit trajectories of atomic-level object locations as a continuous function of time. Our approach is specially tailored to handle temporal analysis of objects that seemingly stochastically disappear and subsequently reappear throughout a sequence. We demonstrate that the proposed method is highly effective and efficient in simulation scenarios, and delivers notable performance improvements in TEM experiments compared to other material science benchmarks.