Spatial Sampling for Image Segmentation

  • Mariano Rivera
  • Oscar Dalmau
  • Washington Mio
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 62)


We present a framework for image segmentation based on the ML estimator. A common hypothesis for explaining the differences among image regions is that they are generated by sampling different Likelihood Functions. We adopt last hypothesis and, additionally, we assume that such samples are i.i.d. Thus, the probability of a model generates the observed pixel value is estimated by computing the likelihood of the sample composed with the surrounding pixels.


Image Segmentation Spatial Sampling Neighborhood Selection Bayesian Regularization Markov Random Field Modeling 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Machine Intell. 23(11), 1222–1239 (2001) 2CrossRefGoogle Scholar
  2. 2.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39, 1–38 (1977) 2MathSciNetGoogle Scholar
  3. 3.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images. IEEE PAMI 6(6), 721–741 (1984) 2zbMATHGoogle Scholar
  4. 4.
    Kohli, P., Torr, P.H.S.: Dynamic graph cuts for efficient inference in Markov random fields. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2079–2088 (2007) 2CrossRefGoogle Scholar
  5. 5.
    Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 26(2), 147–159 (2004) 2CrossRefGoogle Scholar
  6. 6.
    Komodakis, N., Tziritas, G., Paragios, N.: Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies. Computer Vision and Image Understanding 112, 14–29 (2008) 2CrossRefGoogle Scholar
  7. 7.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis. Springer-Verlag, Tokyo (2001) 2, 4zbMATHGoogle Scholar
  8. 8.
    Marroquin, J.L., Velazco, F., Rivera, M., Nakamura, M.: Probabilistic solution of ill-posed problems in computational vision. IEEE Trans. Pattern Anal. Machine Intell. 23, 337–348 (2001) 2, 4CrossRefGoogle Scholar
  9. 9.
    Olsson, C., Eriksson, A.P., Kahl, F.: Improved spectral relaxation methods for binary quadratic optimization problems. Computer Vision and Image Understanding 112, 30–38 (2008) 2CrossRefGoogle Scholar
  10. 10.
    Perona, P., Malik, J.: Scale-space and edge-detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990) 4CrossRefGoogle Scholar
  11. 11.
    Rivera, M., Ocegueda, O., Marroquin, J.L.: Entropy-controlled quadratic Markov measure field models for efficient image segmentation. IEEE Trans. Image Processing 8(12), 3047–3057 (Dec 2007) 2CrossRefMathSciNetGoogle Scholar
  12. 12.
    Terzopoulos, D.: Regularization of inverse visual problems involving discontinuities. IEEE Trans. Pattern Anal. Mach. Intell. 8(4), 413–424 (1986) 4CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Mariano Rivera
    • 1
  • Oscar Dalmau
    • 1
  • Washington Mio
    • 2
  1. 1.Centro de Investigacion en Matematicas A.CGuanajuatoMexico
  2. 2.Florida State UniversityTallahasseeUSA

Personalised recommendations