H. Van Brussel
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
Christian Eitzinger
W. Heidl
E. Lughofer
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/ |
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