A 3D convolutional variational autoencoder trained on bulk spectra detects synthetic Fe L-edge peak-shift anomalies in EELS spectrum images with higher F1 scores than PCA.
Computer Vision-aided Atom Tracking in STEM Imaging
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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.
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Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational Autoencoders
A 3D convolutional variational autoencoder trained on bulk spectra detects synthetic Fe L-edge peak-shift anomalies in EELS spectrum images with higher F1 scores than PCA.