• Corpus ID: 255998791

Feature detection and hypothesis testing for extremely noisy nanoparticle images using topological data analysis

@inproceedings{Thomas2022FeatureDA,
  title={Feature detection and hypothesis testing for extremely noisy nanoparticle images using topological data analysis},
  author={Andrew M. Thomas and Peter A. Crozier and Yuchen Xu and David S. Matteson},
  year={2022}
}
We propose a flexible algorithm for feature detection and hypothesis testing in images with ultra low signal-to-noise ratio using cubical persistent homology. Our main application is in the identification of atomic columns and other features in transmission electron microscopy (TEM). Cubical persistent homology is used to identify local minima and their size in subregions in the frames of nanoparticle videos, which are hypothesized to correspond to relevant atomic features. We compare the… 

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