Extended machine vision-control capabilities using digital holography and transformer neural networks

Louis Andréoli; Stéphane Cuenat; Antoine N. André; Patrick Sandoz; Raphaël Couturier; Guillaume J. Laurent; Maxime Jacquot

doi:10.1117/12.2607116

9 March 2022 Extended machine vision-control capabilities using digital holography and transformer neural networks

Louis Andréoli, Stéphane Cuenat, Antoine N. André, Patrick Sandoz, Raphaël Couturier, Guillaume J. Laurent, Maxime Jacquot

Author Affiliations +

Proceedings Volume PC12019, AI and Optical Data Sciences III; PC1201903 (2022) https://doi.org/10.1117/12.2607116
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

We develop a novel high‐profile application of machine learning techniques by elevating digital holography and sensing in robotics to a new level. The extraction of unknown metrics such as focusing distance and in plane positioning without full image restoration from digital holograms is performed by pre‐processing approach in space‐domain and/or in Fourier‐domain, including real‐time constraints. Measuring a single hologram, we successfully determine the axial distance of a complex object to the 10x microscope objective over a range of 100 µm with an accuracy of 1.25 µm. We apply a machine learning technique to the hologram to speed up tracking in the plane of the pseudo-periodic target position up to several tens of frames per second (fps). Such high frame rates enable real-time processing in many different application scenarios.

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Citation Download Citation

Louis Andréoli, Stéphane Cuenat, Antoine N. André, Patrick Sandoz, Raphaël Couturier, Guillaume J. Laurent, and Maxime Jacquot "Extended machine vision-control capabilities using digital holography and transformer neural networks", Proc. SPIE PC12019, AI and Optical Data Sciences III, PC1201903 (9 March 2022); https://doi.org/10.1117/12.2607116

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