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Automatic visual inspection based on trajectory data

 
: Maier, Georg; Mürdter, N.; Gruna, Robin; Längle, Thomas; Beyerer, Jürgen

:
Fulltext urn:nbn:de:0011-n-5376291 (10 MByte PDF)
MD5 Fingerprint: a900b523ed2fec3fa2e06c7322b639a6
Created on: 21.3.2019


Beyerer, Jürgen (Ed.); Puente Leon, Fernando (Ed.); Längle, Thomas (Ed.):
OCM 2019, 4th International Conference on Optical Characterization of Materials : March 13th-14th, 2019, Karlsruhe, Germany
Karlsruhe: KIT Scientific Publishing, 2019
ISBN: 978-3-7315-0864-9
ISBN: 3-7315-0864-8
DOI: 10.5445/KSP/1000087509
pp.87-97
International Conference on Optical Characterization of Materials (OCM) <4, 2019, Karlsruhe>
English
Conference Paper, Electronic Publication
Fraunhofer IOSB ()
machine vision; motion feature; tracking

Abstract
Automatic inspection tasks have successfully been implemented in several industrial fields and are of growing importance. Visual inspection using optical sensors is wide spread due to the vast variety of different sensors, observable features and comparatively low prices. It seems obvious that corresponding systems are blind towards mechanical features and inspection of those typically requires highly specialized, inflexible and costly systems. Recently, we have shown in the context of sensor-based sorting that tracking objects over a time period allows deriving motion-based features which potentially enable discrimination of optically identical objects, although an optical sensor is used. In this paper, we take one step back from the specific application and study the classification of test objects based on their trajectories. The objects are observed while receiving a certain impulse. We further refrain from manually designing features but use raw coordinates as extracted from a series of images. The success of the method is demonstrated by discriminating spheres made of similar plastic types while bouncing off a plane.

: http://publica.fraunhofer.de/documents/N-537629.html