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Convolutional Neural Networks Applied to Inline Particle Holography

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Abstract

Three-dimensional particle positioning from inline holograms is performed using convolutional neural networks. The faster R-CNN architecture is implemented for multi-particle identification and lateral positioning, and a second network estimates the depth position. Supervised learning is used to train the network using simulated holograms.

© 2020 The Author(s)

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