Skip to main content

Automated Visual Inspection of Glass Bottles Using Adapted Median Filtering

  • Conference paper
Book cover Image Analysis and Recognition (ICIAR 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3212))

Included in the following conference series:

Abstract

This work presents a digital image processing technique for the automated visual inspection of glass bottles based on a well-known method used for inspecting aluminium die castings. The idea of this method is to generate median filters adapted to the structure of the object under test. Thus, a “defect-free” reference image can be estimated from the original image of the inspection object. The reference image is compared with the original one, and defects are detected when the difference between them is considerable. The configuration of the filters is performed off-line including a priori information about real defect-free images. In the other hand, the filtering self is performed on-line. Thus, a fast on-line inspection is ensured. According to our experiments, the detection performance in glass bottles was 85% and the false alarms rate was 4%. Additionally, the processing time was only 0.3s/image.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Newman, T., Jain, A.: A survey of automated visual inspection. Computer Vision and Image Understanding 61, 231–262 (1995)

    Article  Google Scholar 

  2. Parker, J.: Defect in glass and their origin. In: First Balkan Conference on Glass Science & Technology, Vollos, Greace (2000)

    Google Scholar 

  3. Firmin, C., Hamad, D., Postaire, J., Zhang, R.: Gaussian neural networks for bottles inspection: a learning procedure. International Journal of Neural System 8, 41–46 (1997)

    Article  Google Scholar 

  4. Hamad, D., Betrouni, M., Biela, P., Postaire, J.: Neural networks inspection system for glass bottles production: A comparative study. International Journal of Pattern Recognition and Artificial Intelligence 12, 505–516 (1998)

    Article  Google Scholar 

  5. Riffard, B., David, B., Firmin, C., Orteu, J., Postaire, J.: Computer vision systems for tuning improvement in glass bottle production: on-line gob control and crack detection. In: Proceedings of the 5th International Conference on Quality Control by Artificial Vision (QCAV-2001), Le Creusot, France (2001)

    Google Scholar 

  6. Mery, D., Jaeger, T., Filbert, D.: A review of methods for automated recognition of casting defects. Insight 44, 428–436 (2002)

    Google Scholar 

  7. Filbert, D., Klatte, R., Heinrich, W., Purschke, M.: Computer aided inspection of castings. In: IEEE-IAS Annual Meeting, Atlanta, USA 1087–1095 (1987)

    Google Scholar 

  8. Castleman, K.: Digital Image Processing. Prentice-Hall, Englewood Cliffs (1996)

    Google Scholar 

  9. Heinrich, W.: Automated Inspection of Castings using X-ray Testing. PhD thesis, Institute for Measurement and Automation, Faculty of Electrical Engineering, Technical University of Berlin (1988) (in German)

    Google Scholar 

  10. Duda, R., Hart, P., Stork, D.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., New York (2001)

    MATH  Google Scholar 

  11. Mery, D.: Flaw simulation in castings inspection by radioscopy. Insight 43, 664–668 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mery, D., Medina, O. (2004). Automated Visual Inspection of Glass Bottles Using Adapted Median Filtering. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2004. Lecture Notes in Computer Science, vol 3212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30126-4_99

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-30126-4_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23240-7

  • Online ISBN: 978-3-540-30126-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics