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A feature descriptor for texture-less object representation using 2D and 3D cues from RGB-D data

: Fischer, Jan; Bormann, Richard; Arbeiter, Georg; Verl, Alexander

Preprint urn:nbn:de:0011-n-2435821 (2.3 MByte PDF)
MD5 Fingerprint: 28995c9e5cfe3375e7e006641a62baff
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Created on: 30.11.2013

IEEE Robotics and Automation Society; Karlsruher Institut für Technologie -KIT-:
IEEE International Conference on Robotics and Automation, ICRA 2013 : Anthropomatics - Technologies for Humans. May 6-10, 2013, Karlsruhe, Germany
New York, NY: IEEE, 2013
ISBN: 978-1-4673-5642-8 (USB Stick)
ISBN: 978-1-4673-5641-1 (Print)
ISBN: 978-1-4673-5643-5 (electronic)
ISBN: 978-1-4673-5640-4 (print)
International Conference on Robotics and Automation (ICRA) <2013, Karlsruhe>
European Commission EC
FP7-ICT; 231143; ECHORD
Conference Paper, Electronic Publication
Fraunhofer IPA ()
RGB-D; HERMES; räumliche Wahrnehmung; 3D mapping; Wahrnehmung; Objekterkennung; Merkmalerkennung

At the core of every object recognition system lies the development and integration of distinct feature descriptors to create object representations robust against varying perspectives or lightning conditions. Recent work has primarily focused on the development of distinct point features. While these features achieve impressive recognition results, point features fail to capture the shape and appearance of an object with less or even without texture. This paper proposes a novel method for the rapid and dense computation of 2D and 3D image cues from RGB-D data to target the recognition of objects without rich texture and a global histogram-based descriptor for the distinct description of object models.