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

  • Authors:
  • Christian Eitzinger;W. Heidl;E. Lughofer;S. Raiser;J. E. Smith;M. A. Tahir;D. Sannen;H. Van Brussel

  • Affiliations:
  • Profactor GmbH, Steyr, Austria;Profactor GmbH, Steyr, Austria;Johannes Kepler University, Linz, Austria;Johannes Kepler University, Linz, Austria;University of the West of England, Bristol, UK;University of the West of England, Bristol, UK;Katholieke Universiteit Leuven, Leuven, Belgium;Katholieke Universiteit Leuven, Leuven, Belgium

  • Venue:
  • Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

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.