Preprint
Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation
ArXiv.org
Cornell University
02/01/2023
DOI: 10.48550/arxiv.2302.00816
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.
Details
- Title: Subtitle
- Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation
- Creators
- Yuchen XuAndrew M ThomasPeter A CrozierDavid S Matteson
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2302.00816
- ISSN
- 2331-8422
- Publisher
- Cornell University
- Number of pages
- 27
- Language
- English
- Date posted
- 02/01/2023
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984446727002771
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