Particle detection using closed-loop active model diagnosis

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

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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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 particle detection in fluorescence microscopy measurements, it can be developed further to apply in different areas such as sequential object segmentation for reliable and efficient product inspection in Industry 4.0.

Original languageEnglish
Title of host publicationAI and Optical Data Sciences III
EditorsBahram Jalali, Ken-ichi Kitayama
Number of pages5
ISBN (Electronic)9781510649095
Publication statusPublished - 2022
EventAI and Optical Data Sciences III 2022 - Virtual, Online
Duration: 20 Feb 202224 Feb 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceAI and Optical Data Sciences III 2022
CityVirtual, Online


  • Active fault diagnosis
  • Auxiliary signal design
  • Fluorescence microscopy
  • Machine vision


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