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Proceedings Paper

Surface analysis of cast aluminum by means of artificial vision and AI-based techniques
Author(s): Carlos Platero; Carlos Fernandez; Pascual Campoy; Rafael Aracil
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Paper Abstract

An architecture for surface analysis of continuous cast aluminum strip is described. The data volume to be processed has forced up the development of a high-parallel architecture for high- speed image processing. An especially suitable lighting system has been developed for defect enhancing in metallic surfaces. A special effort has been put in the design of the defect detection algorithm to reach two main objectives: robustness and low processing time. These goals have been achieved combining a local analysis together with data interpretation based on syntactical analysis that has allowed us to avoid morphological analysis. Defect classification is accomplished by means of rule-based systems along with data-based classifiers. The use of clustering techniques is discussed to perform partitions in Rn by SOM, divergency methods to reduce the feature vector applied to the data-based classifiers. The combination of techniques inside a hybrid system leads to near 100% classification success.

Paper Details

Date Published: 21 February 1996
PDF: 11 pages
Proc. SPIE 2665, Machine Vision Applications in Industrial Inspection IV, (21 February 1996); doi: 10.1117/12.232250
Show Author Affiliations
Carlos Platero, Univ. Politecnica de Madrid (Spain)
Carlos Fernandez, Univ. Politecnica de Madrid (Spain)
Pascual Campoy, Univ. Politecnica de Madrid (Spain)
Rafael Aracil, Univ. Politecnica de Madrid (Spain)

Published in SPIE Proceedings Vol. 2665:
Machine Vision Applications in Industrial Inspection IV
A. Ravishankar Rao; Ning Chang, Editor(s)

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