Closed-loop active object recognition with constrained illumination power

Autor:	Noom, J., Soloviev, O.A., Smith, C.S., Verhaegen, M.H.G., Kehtarnavaz, Nasser, Carlsohn, Matthias F.
Rok vydání:	2022
Předmět:	Auxiliary signal design Machine Vision Computational Tomography Active fault diagnosis Medical imaging Industry 4.0
Zdroj:	Proc. SPIE 12102, Real-Time Image Processing and Deep Learning 2022 Proceedings Real-Time Image Processing and Deep Learning 2022 Real-Time Image Processing and Deep Learning 2022
DOI:	10.1117/12.2618750
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c41a9abb4c03afbb2ecde0cc4eb295b4 Zobrazit plný text záznamu