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National Repository of Grey Literature 2 records found  Search took 0.00 seconds. 
Quantitative Digital Holographic Microscopy using machine learning
 Duša, Martin ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
This thesis presents machine learning methods for determining the parameters of micro and nano particles from digital holographic microscopy images. In the theoretical part the principles of hologram imaging, holographic microscopy and the similarity between Mie theory and hologram are presented. The second part of the theoretical review is devoted to machine learning methods used in determining the quantitative information of particles. The practical part is focused on the design of a procedure for determining the position, refractive index and radius using the U-Net architecture implemented in PyTorch and DeepTrack 2.1. The results of the proposed methodologies are discussed at the end of the paper.
Detailed record
Quantitative Digital Holographic Microscopy using machine learning
 Duša, Martin ; Kolář, Radim (referee) ; Vičar, Tomáš (advisor)
This thesis presents machine learning methods for determining the parameters of micro and nano particles from digital holographic microscopy images. In the theoretical part the principles of hologram imaging, holographic microscopy and the similarity between Mie theory and hologram are presented. The second part of the theoretical review is devoted to machine learning methods used in determining the quantitative information of particles. The practical part is focused on the design of a procedure for determining the position, refractive index and radius using the U-Net architecture implemented in PyTorch and DeepTrack 2.1. The results of the proposed methodologies are discussed at the end of the paper.
Detailed record

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