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Improving material characterization in sensor-based sorting by utilizing motion information

: Maier, Georg; Pfaff, F.; Becker, F.; Pieper, C.; Gruna, Robin; Noack, B.; Kruggel-Emden, H.; Längle, Thomas; Hanebeck, U.D.; Wirtz, S.; Scherer, V.; Beyerer, Jürgen

Fulltext urn:nbn:de:0011-n-4389105 (7.3 MByte PDF)
MD5 Fingerprint: 20414df06cfbbb7f3939c0abf44f62f6
Created on: 30.3.2017

Beyerer, Jürgen (Ed.); Puente Leon, Fernando (Ed.); Längle, Thomas (Ed.):
OCM 2017, 3rd International Conference on Optical Characterization of Materials : 22-23 March 2017, Karlsruhe, Germany
Karlsruhe: KIT Scientific Publishing, 2017
ISBN: 978-3-7315-0612-6
ISBN: 3-7315-0612-2
International Conference on Optical Characterization of Materials (OCM) <3, 2017, Karlsruhe>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
optical inspection; sensor-based sorting; multitarget tracking; classification

Sensor-based sorting provides state-of-the-art solutions for sorting of cohesive, granular materials. Systems are tailored to a task at hand, for instance by means of sensors and implementation of data analysis. Conventional systems utilize scanning sensors which do not allow for extraction of motion related information of objects contained in a material feed. Recently, usage of area-scan cameras to overcome this disadvantage has been proposed. Multitarget tracking can then be used in order to accurately estimate the point in time and position at which any object will reach the separation stage. In this paper, utilizing motion information of objects which can be retrieved from multitarget tracking for the purpose of classification is proposed. Results show that corresponding features can significantly increase classification performance and eventually decrease the detection error of a sorting system.