@inproceedings{60cb76235de34a28931efba0ad517128,
title = "Closed-loop active object recognition with constrained illumination power",
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.",
keywords = "Active fault diagnosis, Auxiliary signal design, Computational Tomography, Industry 4.0, Machine Vision, Medical imaging",
author = "Jacques Noom and Oleg Soloviev and Carlas Smith and Michel Verhaegen",
year = "2022",
doi = "10.1117/12.2618750",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Nasser Kehtarnavaz and Carlsohn, {Matthias F.}",
booktitle = "Proceedings Real-Time Image Processing and Deep Learning 2022",
address = "United States",
note = "Real-Time Image Processing and Deep Learning 2022 ; Conference date: 06-06-2022 Through 12-06-2022",
}