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High-throughput sensor-based sorting via approximate computing

: Maier, Georg; Bromberger, M.; Längle, Thomas; Karl, W.

Heizmann, M. (Hrsg.); Längle, Thomas (Hrsg.); Puente Leon, F. (Hrsg.):
Forum Bildverarbeitung 2016 : 01.-02. Dezember 2016, Karlsruhe
Karlsruhe: KIT Scientific Publishing, 2016
ISBN: 978-3-7315-0587-7
ISBN: 3-7315-0587-8
Forum Bildverarbeitung <2016, Karlsruhe>
Fraunhofer IOSB ()
sensor-based sorting; optical sorting; approximate computing

Sensor-based sorting provides solutions for separating cohesive, granular materials. In order to reliably locate the position of material objects with deviating velocity, perception and separation shall be close together. This in turn poses challenges on the data analysis systems, since available processing time depends on this distance and the velocity of the object. Whenever the sorting decision for an object cannot be derived in time, no information about this object is taken into account, potentially leading to a sorting error. In this paper, we present an analysis of the impact of this distance and an approach which allows utilizing information about an object before the final classification result is available. Therefore, we apply the concept of anytime algorithms to a decision tree-based classifier. First results suggest that the approach can indeed increase the sorting quality for complex objects for which the deadline would else not be met.