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Local Statistic Based Region Segmentation with Automatic Scale Selection

  • Jérome Piovano
  • Théodore Papadopoulo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5303)

Abstract

Recently, new segmentation models based on local information have emerged. They combine local statistics of the regions along the contour (inside and outside) to drive the segmentation procedure. Since they are based on local decisions, these models are more robust to local variations of the regions of interest (contrast, noise, blur, ...). They nonetheless also introduce some new difficulties which are inherent to the fact of basing a global property (the segmentation) on pure local decisions. This papers explores some of those difficulties and proposes some possible corrections. Results on both 2D and 3D data are compared to those obtained without these corrections.

Keywords

Image Segmentation Active Contour Image Edge Active Contour Model Homogeneous Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Aubert, G., Barlaud, M., Faugeras, O., Jehan-Besson, S.: Image segmentation using active contours: Calculus of variations or shape gradients? SIAM Journal of Applied Mathematics 63(6), 2128–2154 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Boykov, Y., Jolly, M.-P.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV, pp. 105–112 (2001)Google Scholar
  3. 3.
    Brox, T., Cremers, D.: On the statistical interpretation of the piecewise smooth Mumford-Shah functional. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 203–213. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Technical report, HP Labs (September 1994); A shorter version appeared at 5th ICCV 1995, Boston (1995)Google Scholar
  5. 5.
    Chan, T., Vese, L.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Charpiat, G., Maurel, P., Pons, J.-P., Keriven, R., Faugeras, O.: Generalized gradients: Priors on minimization flows. The International Journal of Computer Vision 73(3), 325–344 (2007)CrossRefGoogle Scholar
  7. 7.
    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(2), 195–215 (2007)CrossRefGoogle Scholar
  8. 8.
    Deriche, R.: Recursively implementing the gaussian and its derivatives. Technical Report 1893, INRIA, Unité de Recherche Sophia-Antipolis (1993)Google Scholar
  9. 9.
    Dervieux, A., Thomasset, F.: A finite element method for the simulation of Rayleigh-Taylor instability. Lecture Notes in Mathematics, vol. 771, pp. 145–159 (1979)Google Scholar
  10. 10.
    Grady, L.: Random walks for image segmentation. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(11), 1768–1783 (2006)CrossRefGoogle Scholar
  11. 11.
    Juan, O., Keriven, R., Postelnicu, G.: Stochastic motion and the level set method in computer vision: Stochastics active contours. International Journal of Computer Vision 69(1), 7–25 (2006)CrossRefGoogle Scholar
  12. 12.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. The International Journal of Computer Vision 1(4), 321–331 (1987)CrossRefGoogle Scholar
  13. 13.
    Kichenassamy, S., Kumar, A., Olver, P., Tannenbaum, A., Yezzi, A.: Gradient flows and geometric active contour models. In: Proceedings of the 5th International Conference on Computer Vision, June 1995, pp. 810–815 (1995)Google Scholar
  14. 14.
    Kim, J., Fisher, J., Yezzi, A., Cetin, M., Willsky, A.: Nonparametric methods for image segmentation using information theory and curve evolution. In: IEEE International Conference on Image Processing, September 2002, pp. 797–800 (2002)Google Scholar
  15. 15.
    Lindeberg, T.: Feature detection with automatic scale selection. The International Journal of Computer Vision 30(2), 77–116 (1998)Google Scholar
  16. 16.
    Morel, J.M., Solimini, S.: Variational Methods in Image Segmentation. In: Progress in Nonlinear Differential Equations and Their Applications, Birkhäuser, Basel (1995)Google Scholar
  17. 17.
    Mory, B., Ardon, R., Thiran, J.-P.: Variational Segmentation using Fuzzy Region Competition and Local Non-Parametric Probability Density Functions. In: IEEE International Conference on Computer Vision (ICCV), Rio, Brazil (2007)Google Scholar
  18. 18.
    Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton–Jacobi formulations. Journal of Computational Physics 79(1), 12–49 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Paragios, N., Deriche, R.: Geodesic active regions: a new paradigm to deal with frame partition problems in computer vision. Journal of Visual Communication and Image Representation, Special Issue on Partial Differential Equations in Image Processing, Computer Vision and Computer Graphics 13(1/2), 249–268 (2002)Google Scholar
  20. 20.
    Piovano, J., Rousson, M., Papadopoulo, T.: Efficient segmentation of piecewise smooth images. In: Sgallari, F., Murli, A., Paragios, N. (eds.) SSVM 2007. LNCS, vol. 4485, pp. 709–720. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  21. 21.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Sciences. Cambridge Monograph on Applied and Computational Mathematics. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  22. 22.
    Sundaramoorthi, G., Yezzi Jr., A.J., Mennucci, A.C.: Sobolev active contours. The International Journal of Computer Vision 73(3), 345–366 (2007)CrossRefzbMATHGoogle Scholar
  23. 23.
    Tsai, A., Yezzi, A.J., Willsky, A.S.: Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification. IEEE Transactions on Image Processing 10(8), 1169–1186 (2001)CrossRefzbMATHGoogle Scholar
  24. 24.
    Vese, L.A., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. The International Journal of Computer Vision 50(3), 271–293 (2002)CrossRefzbMATHGoogle Scholar
  25. 25.
    Wohrer, A.: Model and large-scale simulator of a biological retina with contrast gain control. Ph.D thesis, University of Nice Sophia-Antipolis (2008)Google Scholar
  26. 26.
    Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing 7(3), 359–369 (1998)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Jérome Piovano
    • 1
  • Théodore Papadopoulo
    • 1
  1. 1.Odyssée Project Team, INRIA Sophia Antipolis - MéditerranéeFrance

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