Closed-loop active object recognition with constrained illumination power

Jacques Noom; Oleg Soloviev; Carlas Smith; Michel Verhaegen

doi:10.1117/12.2618750

27 May 2022 Closed-loop active object recognition with constrained illumination power

Jacques Noom, Oleg Soloviev, Carlas Smith, Michel Verhaegen

Proceedings Volume 12102, Real-Time Image Processing and Deep Learning 2022; 1210203 (2022) https://doi.org/10.1117/12.2618750
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Some applications require high level of image-based classification certainty while keeping the total illumination energy as low as possible. Examples are minimally invasive visual inspection in Industry 4.0, and medical imaging systems such as computed tomography, in which the radiation dose should be kept "As Low As is Reasonably Achievable". We introduce a sequential object recognition scheme aimed at minimizing phototoxicity or bleaching while achieving a predefined level of decision accuracy. The novel online procedure relies on approximate weighted Bhattacharyya coefficients for determination of future inputs. Simulation results on the MNIST handwritten digit database show how the total illumination energy is decreased with respect to a detection scheme using constant illumination.

Conference Presentation

Citation Download Citation

Jacques Noom, Oleg Soloviev, Carlas Smith, and Michel Verhaegen "Closed-loop active object recognition with constrained illumination power", Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022, 1210203 (27 May 2022); https://doi.org/10.1117/12.2618750

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