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Quality inspection of veneer using soft-computing methods

Stolpmann, Alexander; Angele, Jürgen and Dooley, Laurence S. (2000). Quality inspection of veneer using soft-computing methods. In: Parmee, I. C. ed. Evolutionary Design and Manufacture: Selected Papers from ACDM '00, Volume 1. London: Springer-Verlag, pp. 363–370.

URL: http://link.springer.com/book/10.1007/978-1-4471-0...
DOI (Digital Object Identifier) Link: https://doi.org/10.1007/978-1-4471-0519-0_30
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Abstract

This paper describes the use of a complex modular image processing system for veneer classification. An introduction into problems that arise when producing and processing veneer is given, namely flaws in the wood and faults in the final product, in this case spring boards for slatted frames.

A system has been developed that detects and classifies the faults. A line-scan camera is used for capturing the veneer images of which the features are extracted with statistical methods. The features are classified with either fuzzy clustering methods or neural networks. Additionally genetic algorithms are used for optimization purposes.

Item Type: Book Section
Copyright Holders: 2000 Springer-Verlag London Limited
ISBN: 1-85233-300-6, 978-1-85233-300-3
Extra Information: Selected Papers from 1st Adaptive Computing in Design and Manufacture (ACDM’00) Conference, Plymouth
Keywords: system optimization with genetic algorithms; neural networks and fuzzy clustering for classification; pattern recognition and image processing; application in timber industry
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 38058
Depositing User: Laurence Dooley
Date Deposited: 07 Aug 2013 08:17
Last Modified: 07 Dec 2018 10:17
URI: http://oro.open.ac.uk/id/eprint/38058
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