Ghita, Ovidiu, Carew, Tim and Whelan, Paul F. ORCID: 0000-0002-2029-1576 (2011) A machine vision system for quality grading of painted slates. In: Batchelor, Bruce G., (ed.) Machine Vision Handbook. Springer-Verlag London Limited, London. ISBN 978-1-84996-169-1
Abstract
The major aim of this chapter is to detail the technology associated with a novel industrial inspection system that is able to robustly identify the visual defects present on the surface of painted slates. The development of a real-time automated slate inspection system proved to be a challenging task since the surface of the slate is painted with glossy dark colours, the slate is characterised by depth profile non-uniformities and it is transported at the inspection line via high-speed conveyors. In order to implement an industrial compliant system, in our design we had to devise a large number of novel solutions including the development of a full customised illumination set-up and the development of flexible image-processing procedures that can accommodate the large spectrum of visual defects that can be present on the slate surface and the vibrations generated by the slate transport system. The developed machine vision system has been subjected to a thorough robustness evaluation and the reported experimental results indicate that the proposed solution can be used to replace the manual procedure that is currently used to grade the painted slates in manufacturing environments.
Metadata
Item Type: | Book Section |
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Refereed: | Yes |
Uncontrolled Keywords: | computer vision; Machine vision; Image processing |
Subjects: | Engineering > Electronic engineering |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering |
Publisher: | Springer-Verlag London Limited |
Copyright Information: | © 2011 Springer |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
ID Code: | 18580 |
Deposited On: | 17 Jul 2013 08:33 by Mark Sweeney . Last Modified 11 Jan 2019 13:35 |
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