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Assessment of the influence of adaptive components in trainable surface inspection systems

Van Brussel, H.; Eitzinger, Christian; Heidl, W.; Lughofer, E.; Smith, Jim; Raiser, S.; Tahir, M. A.; Sannen, D.

Authors

H. Van Brussel

Christian Eitzinger

W. Heidl

E. Lughofer

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Jim Smith James.Smith@uwe.ac.uk
Professor in Interactive Artificial Intelligence

S. Raiser

M. A. Tahir

D. Sannen



Abstract

In this paper, we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification result. A major contribution of this paper is a quantitative assessment of how incorporating adaptivity into the feature calculation, the feature pre-processing, and into the classifiers themselves, influences the final image classification performance. Hereby, results achieved on a range of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are presented. © Springer-Verlag 2009.

Citation

Van Brussel, H., Eitzinger, C., Heidl, W., Lughofer, E., Raiser, S., Smith, J., …Sannen, D. (2010). Assessment of the influence of adaptive components in trainable surface inspection systems. Machine Vision and Applications, 21(5), 613-626. https://doi.org/10.1007/s00138-009-0211-1

Journal Article Type Article
Publication Date Aug 1, 2010
Journal Machine Vision and Applications
Print ISSN 0932-8092
Electronic ISSN 1432-1769
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 21
Issue 5
Pages 613-626
DOI https://doi.org/10.1007/s00138-009-0211-1
Keywords adaptive components, surface inspection systems
Public URL https://uwe-repository.worktribe.com/output/976413
Publisher URL http://dx.dio.org/10.1007/s00138-009-0211-1
Related Public URLs http://www.springerlink.com/content/ku12vx63317306w2/