Skip to main content

Comparison of Appearance-Based and Geometry-Based Bubble Detectors

  • Conference paper
Computer Vision and Graphics (ICCVG 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8671))

Included in the following conference series:

Abstract

Bubble detection is a complicated tasks since varying lighting conditions changes considerably the appearance of bubbles in liquid. The two common techniques to detect circular objects such as bubbles, the geometry-based and appearance-based approaches, have their advantages and weaknesses. The geometry-based methods often fail to detect small blob-like bubbles that do not match the used geometrical model, and appearance-based approaches are vulnerable to appearance changes caused by, e.g., illumination. In this paper, we compare a geometry-based concentric circular arrangements (CCA) and appearance-based sliding window methods as well as their combinations in terms of bubble detection, gas volume computation, and size distribution estimation. The best bubble detection performance was achieved with the sliding window method whereas the most precise volume estimate was produced by the CCA method. The combination of the two approaches gave only a minor advantage compared to the base methods.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

Similar content being viewed by others

References

  1. Canny, J.: A computational approach to edge detection. PAMI 8(6), 679–698 (1986)

    Article  Google Scholar 

  2. Dence, C.W., Reeve, D.W.: Pulp Bleaching, Principles and Practice. TAPPI (1996)

    Google Scholar 

  3. Dominguez, R.A., Corkidi, G.: Automated recognition of oil drops in images of multiphase dispersions via gradient direction pattern. In: CISP, vol. 3, pp. 1209–1213 (2011)

    Google Scholar 

  4. Duda, R., Hart, P.: Using the hough transform to detect lines and curves in pictures. Comm ACM, pp. 11–15 (1972)

    Google Scholar 

  5. Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitt. with appl. to image anal. and autom. cartogr. Comm ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  6. Herout, A., Jošth, R., Juránek, R., Havel, J., Hradiš, M., Zemčík, P.: Real-time object detection on cuda. JRIP (2011)

    Google Scholar 

  7. Leavers, V.: Which Hough Transform? GMIP: IU 58(2), 250–264 (1993)

    Google Scholar 

  8. Press, W., Flannery, B., Teukolsky, S., Vetterling, W.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press (1992)

    Google Scholar 

  9. Ronneberger, O., Wang, Q., Burkhardt, H.: Fast and robust segm. of sph.l particles in vol. data sets from brightfield microsc. In: Proc. ISBI, pp. 372–375 (2008)

    Google Scholar 

  10. Strokina, N., Matas, J., Eerola, T., Lensu, L., Kälviäinen, H.: Detection of bubbles as concentric circular arrangements. In: ICPR, pp. 2655–2659 (2012)

    Google Scholar 

  11. Strokina, N.: Machine vision methods for process measurements in pulping. Ph.D. thesis, LUT (2013)

    Google Scholar 

  12. Viola, P., Jones, M.J.: Robust real-time face detection. IJCV (2004)

    Google Scholar 

  13. Šochman, J., Matas, J.: Waldboost - learning for time constrained sequential detection. In: CVPR (2005)

    Google Scholar 

  14. Zabulis, X., Papara, M., Chatziargyriou, A., Karapantsios, T.D.: Detection of densely dispersed spherical bubbles in dig. images based on a templ. matching technique. appl. to wet foams. Colloids and Surfaces A: PEA 309, 96–106 (2007)

    Article  Google Scholar 

  15. Zemčík, P., Juránek, R., Musil, M., Musil, P., Hradiš, M.: High performance architecture for object detection in streamed videos. In: Proc ICFPLA (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Strokina, N., Juránek, R., Eerola, T., Lensu, L., Zemčik, P., Kälviäinen, H. (2014). Comparison of Appearance-Based and Geometry-Based Bubble Detectors. In: Chmielewski, L.J., Kozera, R., Shin, BS., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2014. Lecture Notes in Computer Science, vol 8671. Springer, Cham. https://doi.org/10.1007/978-3-319-11331-9_73

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11331-9_73

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11330-2

  • Online ISBN: 978-3-319-11331-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics