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RNN-accelerated Experimental Design for Chromatic Confocal Measurement : Technical Report IES-2016-02

Luo, Ding

With decades of research and development, confocal microscopes have been the work horse of scientific and industrial 3D measurement. However, due to its requirement for axial scanning, its range of application is limited by its slow measurement speed. Chromatic confocal measurement systems have been developed to eliminate the need for mechanical scanning. Nevertheless, they are still bottle-necked by the transfer and processing of densely sampled spectral data. In this article, Bayesian experimental design is applied to the chromatic confocal measurement scheme,
allowing for more efficient spectral sampling. Recurrent neural network (RNN) is trained to approximate full Bayesian experimental design with much less computation. Simulations have demonstrated that experimental
design approximated by RNN provides better results than an equidistant sampling scheme and performance close to full Bayesian experimental design.

Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Proceedingsbeitrag
Jahr 2017
Sprache Englisch
Identifikator ISBN: 978-3-7315-0678-2
ISSN: 1863-6489
URN: urn:nbn:de:swb:90-723487
KITopen ID: 1000072348
Erschienen in Proceedings of the 2016 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision an Fusion Laboratory. Ed.: J. Beyerer
Verlag KIT Scientific Publishing, Karlsruhe
Seiten 17-29
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 33
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