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An Intelligent System for Analyzing Welding Defects using Image Retrieval Techniques

Pein, Raoul Pascal, Lu, Joan, Stav, John Birger, Xu, Qiang, Uran, Miro and Mráz, Luboš (2009) An Intelligent System for Analyzing Welding Defects using Image Retrieval Techniques. In: 19th International Conference on Flexible Automation and Intelligent Manufacturing 2009 (FAIM 2009). Curran Associates, pp. 939-946. ISBN 9781615676279

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    Abstract

    The development of new approaches in image processing and retrieval provides several opportunities in supporting in different
    domains. The group of welding engineers frequently needs to conduct visual inspections to assess the quality of welding products.
    It is investigated, if this process can be supported by different kinds of software. Techniques from a generic CBIR system have
    been successfully used to cluster welding photographs according to the severeness of visual faults. Similarity algorithms were
    used to automatically spot faults, such as cracks and gas pores.

    Item Type: Book Chapter
    Additional Information: Paper presented at Proceedings of the 19th International Conference on Flexible Automation and Intelligent Manufacturing (FAIM), 5th-8th July 2009, Teesside University
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Diagnostic Engineering Research Centre
    School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
    School of Computing and Engineering > Informatics Research Group
    School of Computing and Engineering > Informatics Research Group > Software Engineering Research Group
    School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
    Related URLs:
    Depositing User: Raoul Pein
    Date Deposited: 18 May 2010 16:27
    Last Modified: 08 Oct 2013 10:40
    URI: http://eprints.hud.ac.uk/id/eprint/7670

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