Machine Vision and Applications

, Volume 21, Issue 5, pp 613–626

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

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

DOI: 10.1007/s00138-009-0211-1

Cite this article as:
Eitzinger, C., Heidl, W., Lughofer, E. et al. Machine Vision and Applications (2010) 21: 613. doi: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.

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Christian Eitzinger
    • 1
  • W. Heidl
    • 1
  • E. Lughofer
    • 2
  • S. Raiser
    • 2
  • J.E. Smith
    • 3
  • M.A. Tahir
    • 3
  • D. Sannen
    • 4
  • H. Van Brussel
    • 4
  1. 1.Profactor GmbHSteyrAustria
  2. 2.Johannes Kepler UniversityLinzAustria
  3. 3.University of the West of EnglandBristolUK
  4. 4.Katholieke Universiteit LeuvenLeuvenBelgium

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