Defect detection using a distributed blackboard architecture

TAIT, R.J., SCHAEFER, G., HOPGOOD, A.A. and NOLLE, L., 2005. Defect detection using a distributed blackboard architecture. In: Proceedings of the 19th European Conference on Modelling and Simulation ECMS 2005. Nottingham, UK: European Council for Modelling and Simulation, pp. 283-287. ISBN 1842331124

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The framework of an automated vision system for the monitoring of quality control is presented. Inspection, which is capable of detecting various forms of defects is achieved by combining distributed artificial intelligence and image processing. The blackboard architecture DARBS (Nolle and Wong 2001) manages the processing of image data via an area of shared memory where the current understanding of the problem evolves. Registration into a common coordinate system and segmentation of reference and sensed images is performed by intelligent agents, which communicate with each other by means of the blackboard. Pixel-level fusion is performed on registered images in order to exploit complementary and redundant data, allowing identification of suspected defects. The difficulties of landmark extraction common to feature-based registration techniques have been replaced by an intensity-based algorithm. Addition or removal of specialised agents is simplified by the blackboard's modular nature

Item Type: Chapter in book
Creators: Tait, R.J., Schaefer, G., Hopgood, A.A. and Nolle, L.
Publisher: European Council for Modelling and Simulation
Place of Publication: Nottingham, UK
Date: 2005
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 09:56
Last Modified: 19 Oct 2015 14:25

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