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RNN-accelerated Experimental Design for Chromatic Confocal Measurement

: Luo, D.

Postprint urn:nbn:de:0011-n-4618230 (2.1 MByte PDF)
MD5 Fingerprint: f96071a12d09a38e594f880382e1e5ce
Erstellt am: 24.8.2017

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2016. Proceedings : Triberg-Nussbach, July, 24 to 29, 2016
Karlsruhe: KIT Scientific Publishing, 2017 (Karlsruher Schriften zur Anthropomatik 33)
ISBN: 978-3-7315-0678-2
DOI: 10.5445/KSP/1000070009
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2016, Triberg-Nussbach>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

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.