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Proceedings Paper

Fast and robust recognition and localization of 2D objects
Author(s): Rainer Otterbach; Rolf Gerdes; R. Kammueller
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Paper Abstract

The paper presents a vision system which provides a robust model-based identification and localization of 2-D objects in industrial scenes. A symbolic image description based on the polygonal approximation of the object silhouettes is extracted in video real time by the use of dedicated hardware. Candidate objects are selected from the model database using a time and memory efficient hashing algorithm. Any candidate object is submitted to the next computation stage which generates pose hypotheses by assigning model to image contours. Corresponding continuous measures of similarity are derived from the turning functions of the curves. Finally, the previous generated hypotheses are verified using a voting scheme in transformation space. Experimental results reveal the fault tolerance of the vision system with regard to noisy and split image contours as well as partial occlusion of objects. THe short cycle time and the easy adaptability of the vision system make it well suited for a wide variety of applications in industrial automation.

Paper Details

Date Published: 9 November 1994
PDF: 12 pages
Proc. SPIE 2247, Sensors and Control for Automation, (9 November 1994); doi: 10.1117/12.193934
Show Author Affiliations
Rainer Otterbach, Univ. Siegen (Germany)
Rolf Gerdes, Univ. Siegen (Germany)
R. Kammueller, Univ. Siegen (Germany)

Published in SPIE Proceedings Vol. 2247:
Sensors and Control for Automation
Markus Becker; R. W. Daniel; Otmar Loffeld, Editor(s)

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