Skip to main content
SpringerLink
Log in

Feature Quarrels: The Dempster-Shafer Evidence Theory for Image Segmentation Using a Variational Framework

  • Conference paper
Computer Vision – ACCV 2010 (ACCV 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6493))

Included in the following conference series:

Abstract

Image segmentation is the process of partitioning an image into at least two regions. Usually, active contours or level set based image segmentation methods combine different feature channels, arising from the color distribution, texture or scale information, in an energy minimization approach. In this paper, we integrate the Dempster-Shafer evidence theory in level set based image segmentation to fuse the information (and resolve conflicts) arising from different feature channels. They are further combined with a smoothing term and applied to the signed distance function of an evolving contour. In several experiments we demonstrate the properties and advantages of using the Dempster-Shafer evidence theory in level set based image segmentation.

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

Access this chapter

Log in via an institution
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. Mumford, D., Shah, J.: Boundary detection by minimizing functionals. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 22–26. IEEE Computer Society Press, Springer (1985)

    Google Scholar 

  2. Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10, 266–277 (2001)

    Article  MATH  Google Scholar 

  3. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour models. International Journal of Computer Vision 1, 321–331 (1988)

    Article  MATH  Google Scholar 

  4. Osher, S., Paragios, N.: Geometric level set method in imaging, vision, and graphics. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  5. Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing, and bayes/mdl for multiband image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence 18, 884–900 (1996)

    Article  Google Scholar 

  6. Rousson, M., Brox, T., Deriche, R.: Active unsupervised texture segmentation on a diffusion based feature space. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, pp. 699–704 (2003)

    Google Scholar 

  7. Cremers, D., Soatto, S.: Motion competition: A variational approach to piecewise parametric motion segmentation. International Journal of Computer Vision 62, 249–265 (2004)

    Article  Google Scholar 

  8. Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 158–175 (1995)

    Article  Google Scholar 

  9. Mena, J.B., Malpica, J.A.: Color image segmentation using the dempster-shafer theory of evidence for the fusion. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (34) 3/W8, pp. 139–144 (2003)

    Google Scholar 

  10. Chaabane, S., Sayadi, M., Fnaiech, F., Brassart, E.: Color image segmentation based on dempster-shafer evidence theory. In: 14th IEEE Mediterranean Electrotechnical Conference, pp. 862–866 (2008)

    Google Scholar 

  11. Chaabane, S.B., Sayadi, M., Fnaiech, F., Brassart, E.: Dempster-Shafer evidence theory for image segmentation: application in cells images. International Journal of Signal Processing (2009)

    Google Scholar 

  12. Adamek, T., O’Connor, N.E.: Using Dempster-Shafer Theory to Fuse Multiple Information Sources in Region-Based Segmentation. In: 2007 IEEE International Conference on Image Processing, pp. 269–272. IEEE, Los Alamitos (2007)

    Google Scholar 

  13. Rombaut, M., Zhu, Y.: Study of Dempster–Shafer theory for image segmentation applications. Image and Vision Computing 20, 15–23 (2002)

    Article  Google Scholar 

  14. Dempster, A.P.: A generalization of bayesian inference. Journal of the Royal Statistical Society. Series B (Methodological) 30, 205–247 (1968)

    MathSciNet  MATH  Google Scholar 

  15. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  16. Cremers, D., Rousson, M., Deriche, R.: A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. International Journal of Computer Vision 72, 195–215 (2007)

    Article  Google Scholar 

  17. Cremers, D., Schmidt, F.R., Barthel, F.: Shape priors in variational image segmentation: Convexity, lipschitz continuity and globally optimal solutions. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2008)

    Google Scholar 

  18. Paragios, N., Rousson, M., Ramesh, V.: Matching distance functions: A shape-to-area variational approach for global-to-local registration. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 775–789. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  19. Heiler, M., Schnörr, C.: Natural image statistics for natural image segmentation. International Journal of Computer Vision 63, 5–19 (2005)

    Article  Google Scholar 

  20. Chan, T., Sandberg, B.Y., Vese, L.: Active contours without edges for vector-valued images. Journal of Visual Communication and Image Representation 11, 130–141 (2000)

    Article  Google Scholar 

  21. Brox, T., Weickert, J.: A tv flow based local scale estimate and its application to texture discrimination. Journal of Visual Communication and Image Representation 17, 1053–1073 (2006)

    Article  Google Scholar 

  22. Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  23. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision., vol. 2, pp. 416–423 (2001)

    Google Scholar 

  24. Haindl, M., Mikeš, S.: Texture segmentation benchmark. In: Proceedings of the 19th International Conference on Pattern Recognition, ICPR, pp. 1–4 (2008)

    Google Scholar 

  25. McGuinness, K., O’Connor, N.E.: A comparative evaluation of interactive segmentation algorithms. Pattern Recognition 43, 434–444 (2010)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Informationsverarbeitung, Leibniz Universität Hannover, Germany

    Björn Scheuermann & Bodo Rosenhahn

Authors
  1. Björn Scheuermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bodo Rosenhahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, Technion, Israel Institute of Technology, 32000, Haifa, Israel

    Ron Kimmel

  2. The University of Auckland, 37 Kohimarama Road, Mission Bay, 1071, Auckland, New Zealand

    Reinhard Klette

  3. National Institute of Informatics, 1018430, Chiyoda, Tokyo, Japan

    Akihiro Sugimoto

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Scheuermann, B., Rosenhahn, B. (2011). Feature Quarrels: The Dempster-Shafer Evidence Theory for Image Segmentation Using a Variational Framework. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19309-5_33

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19308-8

  • Online ISBN: 978-3-642-19309-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation