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.

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.

• 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/