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Automatic selection of optical filters for classification in hyperspectral images

: Richter, M.

Volltext urn:nbn:de:0011-n-3262734 (225 KByte PDF)
MD5 Fingerprint: 2f3e574fd5168e3ccca8a1f7fd0c9d5b
Erstellt am: 10.2.2015

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2013. Proceedings : July, 21 - 27, 2013, Triberg-Nussbach in Germany
Karlsruhe: KIT Scientific Publishing, 2014 (Karlsruher Schriften zur Anthropomatik 17)
ISBN: 978-3-7315-0212-8
ISBN: 3-7315-0212-7
DOI: 10.5445/KSP/1000040668
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2013, Triberg-Nussbach>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

The color of a material is one of the most frequently used feature in automated visual inspection systems. While it is sufficient for many "easy" tasks, more complex materials such as food-stuffs and minerals usually require more complex features. Spectral “signatures” in the near infrared or UV spectrum have proven useful, but hyperspectral imaging devices are still too costly and too slow for industrial application. Therefore, off-the-shelve cameras and optical filters are used to extract characteristic features from the spectra. While the visual inspection community has acknowledged the benefits of this method, relatively few works are concerned with automatic selection of suitable filters. In a novel approach, filter selection is generalized as feature selection problem. In contrast to existing methods, this method can be used to select the best out of a large given set of filters, e.g. from a catalogue. This meta-method is exemplified by application of feature selection methods based on linear discriminant analysis, information theory and boosting.