Particle detection using closed-loop active model diagnosis

Jacques Noom; Oleg Soloviev; Carlas Smith; Hieu Thao Nguyen; Michel Verhaegen

doi:10.1117/12.2605452

2 March 2022 Particle detection using closed-loop active model diagnosis

Jacques Noom, Oleg Soloviev, Carlas Smith, Hieu Thao Nguyen, Michel Verhaegen

Author Affiliations +

Proceedings Volume 12019, AI and Optical Data Sciences III; 120190F (2022) https://doi.org/10.1117/12.2605452
Event: SPIE OPTO, 2022, San Francisco, California, United States

Abstract

We demonstrate a novel closed-loop input design technique on the detection of particles in an imaging system such as a fluorescence microscope. The probability of misdiagnosis is minimized while constraining the input energy such that for instance phototoxicity is reduced. The key novelty of the closed-loop design is that each next input is designed based on the most recent information. Using updated hypothesis probabilities, the input energy distribution is optimized for detection such that unresolved pixels have increased illumination next image acquisition. As compared to conventional open-loop, the results show that (regions of) particles are diagnosed using less energy in the closed-loop approach. Besides the closed-loop approach being viable for the initialization of fluorescence microscopy measurements, it is the next step to sequential object segmentation for reliable and efficient product inspection in Industry 4.0.

Conference Presentation

Citation Download Citation

Jacques Noom, Oleg Soloviev, Carlas Smith, Hieu Thao Nguyen, and Michel Verhaegen "Particle detection using closed-loop active model diagnosis", Proc. SPIE 12019, AI and Optical Data Sciences III, 120190F (2 March 2022); https://doi.org/10.1117/12.2605452

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