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arXiv · 2302.00816

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

Yuchen Xu·Andrew M. Thomas·Peter A. Crozier·David S. Matteson
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Abstract

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.

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Yuchen Xu, Andrew M. Thomas, Peter A. Crozier, David S. Matteson. 2023-02-02. Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation. https://doi.org/10.1109/tip.2025.3543138

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