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Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data
The preset parameters of the 3D model together with the simulated STEM images serve as a database for the training of convolutional neural networks, which can be used to determine the parameters of the underlying 3D model and, consequently, to predict 3D structures of hetero-aggregates from 2D STEM images.
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series
Image-based computational fluid dynamics have long played an important role in leveraging knowledge and understanding of several physical phenomena.
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