Skip to main content

A Note on the Discrete Binary Mumford-Shah Model

  • Conference paper
Book cover Computer Vision/Computer Graphics Collaboration Techniques (MIRAGE 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4418))

Abstract

This paper is concerned itself with the analysis of the two-phase Mumford-Shah model also known as the active contour without edges model introduced by Chan and Vese. It consists of approximating an observed image by a piecewise constant image which can take only two values. First we show that this model with the L 1-norm as data fidelity yields a contrast invariant filter which is a well known property of morphological filters. Then we consider a discrete version of the original problem. We show that an inclusion property holds for the minimizers. The latter is used to design an efficient graph-cut based algorithm which computes an exact minimizer. Some preliminary results are presented.

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. Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms and Applications. Prentice-Hall, Englewood Cliffs (1993)

    Google Scholar 

  2. Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  3. Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: Proceedings of International Conference on Computer Vision, pp. 105–112 (2001)

    Google Scholar 

  4. Boykov, Y., Kolmogorov, V.: Computing geodesic and minimal surfaces via graph cuts. In: International Conference on Computer Vision, vol. 1, pp. 26–33 (2003)

    Google Scholar 

  5. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(9), 1124–1137 (2004)

    Article  Google Scholar 

  6. Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), 1222–1239 (2001)

    Article  Google Scholar 

  7. Bresson, X., et al.: Global minimizers of the active contour/snake model. Technical Report 05-04, UCLA CAM Report (2005)

    Google Scholar 

  8. Caselles, V., Chambolle, A.: Anistropic curvature-driven flow of convex sets. Technical Report 528, CMAP Ecole Polytechnique (2004)

    Google Scholar 

  9. Chambolle, A.: Total variation minimization and a class of binary mrf models. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds.) EMMCVPR 2005. LNCS, vol. 3757, pp. 136–152. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  10. Chan, T.F., Esedoglu, S., Nikolova, M.: Finding the global minimum for binary image restoration. In: Proceedings of the ICIP 2005, Genova, Italy, pp. 121–124 (2005)

    Google Scholar 

  11. Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models. Technical Report 54, UCLA (2004)

    Google Scholar 

  12. Chan, T.F., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2002)

    Article  Google Scholar 

  13. Cormen, T.H., et al.: Introduction to Algorithms. MIT Press, Cambridge (2001)

    MATH  Google Scholar 

  14. Darbon, J.: Composants Logiciels et Algorithmes de minimisation exacte d’énergies dédiś au traitement des images. PhD thesis, Ecole Nationale Supérieure des Télécommunications (October 2005)

    Google Scholar 

  15. Darbon, J.: Total Variation minimization with L 1 data fidelity as a contrast invariant filter. In: Proceedings of the 4th IEEE International Symposium on Image and Signal Processing and Analysis (ISPA 2005), Zagreb, Croatia, September 2005, IEEE, Los Alamitos (2005)

    Google Scholar 

  16. Darbon, J., Sigelle, M.: A fast and exact algorithm for Total Variation minimization. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 351–359. Springer, Heidelberg (2005)

    Google Scholar 

  17. Darbon, J., Sigelle, M.: Image restoration with discrete constrained Total Variation part I: Fast and exact optimization. Journal of Mathematical Imaging and Vision, Online First (2005)

    Google Scholar 

  18. Djurić, P.M., Huang, Y., Ghirmai, T.: Perfect sampling: A review and applications to signal processing. IEEE Signal Processing 50(2), 345–356 (2002)

    Article  Google Scholar 

  19. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 6(6), 721–741 (1984)

    Article  MATH  Google Scholar 

  20. Greig, D., Porteous, B., Seheult, A.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistics Society 51(2), 271–279 (1989)

    Google Scholar 

  21. Guichard, F., Morel, J.-M.: Image Iterative Smoothing and PDE’s. Please write email to fguichard@poseidon-tech.com (2000)

    Google Scholar 

  22. Guichard, F., Morel, J.M.: Mathematical morphology ”almost everywhere”. In: Proceedings of Internationnal Symposium on Mathematical Morpholy, April 2002, pp. 293–303. CSIRO Publishing, Collingwood (2002)

    Google Scholar 

  23. He, L., Osher, S.: Solving the chan-vese model by a multuphase level set algorithm based on the topological derivative. Technical Report CAM 06-56, University of California, Los Angeles (UCLA) (October 2006)

    Google Scholar 

  24. Hochbaum, D.S.: An efficient algorithm for image segmentation, markov random fields and related problems. Journal of the ACM 48(2), 686–701 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  25. Kolmogorov, V., Zabih, R.: What energy can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 147–159 (2004)

    Article  Google Scholar 

  26. Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Comm. on Pure and Applied Mathematics 42, 577–685 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  27. Osher, S., Paragios, N.: Geometric Level Set Methods. Springer, Heidelberg (2003)

    Book  MATH  Google Scholar 

  28. Propp, J.G., Wilson, D.B.: Exact sampling with coupled Markov chains and statistical mechanics. Random Structures and Algorithms 9(1), 223–252 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  29. Serra, J.: Image Analysis and Mathematical Morphology. Academic Press, London (1988)

    Google Scholar 

  30. Song, B., Chan, T.F.: A fast algorithm for level set based optimization. Technical Report CAM 02-68, University of California, Los Angeles (UCLA) (December 2002)

    Google Scholar 

  31. Vese, L., Chan, T.F.: A mutiphase level set framework for image segmentation using the Mumford-Shah model. International Journal of Computer Vision 50(3), 266–277 (2002)

    Article  Google Scholar 

  32. Winkler, G.: Image Analysis, Random Fields and Dynamic Monte Carlo Methods, 2nd edn. Applications of mathematics. Springer, Heidelberg (2003)

    Google Scholar 

  33. Zalesky, B.A.: Network Flow Optimization for Restoration of Images. Journal of Applied Mathematics 2(4), 199–218 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

André Gagalowicz Wilfried Philips

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Darbon, J. (2007). A Note on the Discrete Binary Mumford-Shah Model. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics Collaboration Techniques. MIRAGE 2007. Lecture Notes in Computer Science, vol 4418. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71457-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71457-6_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71456-9

  • Online ISBN: 978-3-540-71457-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics