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

Christian Eitzinger, Wolfgang Heidl, Edwin Lughofer, Stefan Raiser, Jim Smith, Muhammad Tahir, Davy Sannen, Hendrik van Brussel

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)613-626
JournalMachine Vision and Applications
Volume21
Issue number5
DOIs
Publication statusPublished - 2010

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