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High-throughput sensor-based sorting via approximate computing

Maier, Georg; Bromberger, Michael; Längle, Thomas; Wolfgang Karl

Abstract (englisch): 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.

Zugehörige Institution(en) am KIT Institut für Technische Informatik (ITEC)
Publikationstyp Proceedingsbeitrag
Jahr 2016
Sprache Englisch
Identifikator ISBN: 978-3-7315-0587-7
URN: urn:nbn:de:swb:90-630242
KITopen ID: 1000063024
Erschienen in Forum Bildverarbeitung 2016. Hrsg.: M. Heizmann
Verlag KIT Scientific Publishing, Karlsruhe
Seiten 99-110
URLs Gesamtwerk
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