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17 March 2023 Computer microvision-based precision motion measurement by digital holographic microscopy and deep transformer neural networks
Stéphane Cuenat, Jesus E. Brito Carcaño, Patrick Sandoz, Raphaël Couturier, Guillaume J. Laurent, Maxime Jacquot
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Proceedings Volume PC12438, AI and Optical Data Sciences IV; PC124380N (2023) https://doi.org/10.1117/12.2647737
Event: SPIE OPTO, 2023, San Francisco, California, United States
Abstract
An area of particular importance in developing advanced imaging techniques concerns 3D motion measurement in small-scale mechatronics and automated microscopy. One major drawback is related to complex motion measurement with 6 degrees of freedom. In the proposed work, the extraction of unknown metrics such as focusing distance, in plane and out-of-plane positioning from digital holograms is performed including real‐time constraints. This work explores extended computer micro-vision capabilities offered by combining digital holographic microscopy (DHM) and last generation of deep learning algorithms such as Vision Transformer (ViT) networks. Our experiments show that the reconstruction in-focus distance can be predicted in DHM with a high accuracy using tiny modified architectures of deep ViT networks and convolutional neural networks (CNN). We compare ViT and Tiny ViT models with deep CNN usually used in digital holography such as VGG16, LeNet and AlexNet.
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Stéphane Cuenat, Jesus E. Brito Carcaño, Patrick Sandoz, Raphaël Couturier, Guillaume J. Laurent, and Maxime Jacquot "Computer microvision-based precision motion measurement by digital holographic microscopy and deep transformer neural networks", Proc. SPIE PC12438, AI and Optical Data Sciences IV, PC124380N (17 March 2023); https://doi.org/10.1117/12.2647737
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KEYWORDS
Digital holography
Microscopy
Motion measurement
Holography
Transformers
Neural networks
3D metrology
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