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Proceedings Paper

Optical determination of material abundances by using neural networks for the derivation of spectral filters
Author(s): Wolfgang Krippner; Felix Wagner; Sebastian Bauer; Fernando Puente León
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Paper Abstract

Using appropriately designed spectral filters allows to optically determine material abundances. While an infinite number of possibilities exist for determining spectral filters, we take advantage of using neural networks to derive spectral filters leading to precise estimations. To overcome some drawbacks that regularly influence the determination of material abundances using hyperspectral data, we incorporate the spectral variability of the raw materials into the training of the considered neural networks. As a main result, we successfully classify quantized material abundances optically. Thus, the main part of the high computational load, which belongs to the use of neural networks, is avoided. In addition, the derived material abundances become invariant against spatially varying illumination intensity as a remarkable benefit in comparison with spectral filters based on the Moore-Penrose pseudoinverse, for instance.

Paper Details

Date Published: 26 June 2017
PDF: 9 pages
Proc. SPIE 10334, Automated Visual Inspection and Machine Vision II, 1033408 (26 June 2017); doi: 10.1117/12.2270237
Show Author Affiliations
Wolfgang Krippner, Karlsruher Institut für Technologie (Germany)
Felix Wagner, Karlsruher Institut für Technologie (Germany)
Sebastian Bauer, Karlsruher Institut für Technologie (Germany)
Fernando Puente León, Karlsruher Institut für Technologie (Germany)


Published in SPIE Proceedings Vol. 10334:
Automated Visual Inspection and Machine Vision II
Jürgen Beyerer; Fernando Puente León, Editor(s)

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