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Proceedings Paper

Evaluation of potential emission spectra for the reliable classification of fluorescently coded materials
Author(s): Siegfried Brunner; Christian Kargel
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Paper Abstract

The conservation and efficient use of natural and especially strategic resources like oil and water have become global issues, which increasingly initiate environmental and political activities for comprehensive recycling programs. To effectively reutilize oil-based materials necessary in many industrial fields (e.g. chemical and pharmaceutical industry, automotive, packaging), appropriate methods for a fast and highly reliable automated material identification are required. One non-contacting, color- and shape-independent new technique that eliminates the shortcomings of existing methods is to label materials like plastics with certain combinations of fluorescent markers ("optical codes", "optical fingerprints") incorporated during manufacture. Since time-resolved measurements are complex (and expensive), fluorescent markers must be designed that possess unique spectral signatures. The number of identifiable materials increases with the number of fluorescent markers that can be reliably distinguished within the limited wavelength band available. In this article we shall investigate the reliable detection and classification of fluorescent markers with specific fluorescence emission spectra. These simulated spectra are modeled based on realistic fluorescence spectra acquired from material samples using a modern VNIR spectral imaging system. In order to maximize the number of materials that can be reliably identified, we evaluate the performance of 8 classification algorithms based on different spectral similarity measures. The results help guide the design of appropriate fluorescent markers, optical sensors and the overall measurement system.

Paper Details

Date Published: 20 May 2011
PDF: 15 pages
Proc. SPIE 8048, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII, 80480I (20 May 2011); doi: 10.1117/12.884001
Show Author Affiliations
Siegfried Brunner, Univ. der Bundeswehr München (Germany)
Christian Kargel, Univ. der Bundeswehr München (Germany)


Published in SPIE Proceedings Vol. 8048:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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