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

Eitzinger, Christian, Heidl, Wolfgang, Lughofer, Edwin, Raiser, Stefan, Smith, Jim, Tahir, Muhammad, Sannen, Davy and van Brussel, Hendrik (2010) Assessment of the influence of adaptive components in trainable surface inspection systems. Machine Vision and Applications, 21 (5). pp. 613-626. ISSN 0932-8092

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Official URL: http://dx.doi.org/10.1007/s00138-009-0211-1

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.

Item Type: Article
Uncontrolled Keywords: Pattern recognition, image processing and computer vision, communications engineering and networks
Subjects: G400 Computer Science
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Ellen Cole
Date Deposited: 09 Dec 2011 10:42
Last Modified: 10 Aug 2015 11:24
URI: http://nrl.northumbria.ac.uk/id/eprint/3951

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