Skip to main content

Image Processing, Markov Chain Approach

  • Conference paper
COMPSTAT

Abstract

A survey of methods in probabilistic image processing based on Markov Chain Monte Carlo is presented. An example concerning the problem of texture segmentation is included.

Partially supported by GA ČR Grant No. 202/96/0731.

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

  • Besag, J. (1986). On the statistical analysis of dirty pictures (with discussion). J. of the Royal Statist. Soc., series B, 48: 259–302

    MathSciNet  MATH  Google Scholar 

  • Chellappa, R. and Jain, A. (eds.) (1993). Markov Random Fields: Theory and Application. Academic Press, Boston San Diego

    Google Scholar 

  • Cross, G.R. and Jain, A.K. (1983). Markov random field texture models. IEEE Trans. PAMI, 5: 25–39

    Article  Google Scholar 

  • Derin, H. and Elliott, H. (1987). Modeling and segmentation of noisy and textures images using random fields. IEEE Trans. PAMI, 9: 39–55

    Article  Google Scholar 

  • Egem, K. (1995). Textured image segmentation based on probabilistic approach. Diploma thesis. Czech Technical University, Prague.

    Google Scholar 

  • Geman, D. and Geman, S. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. PAMI, 6: 721–741

    Article  MATH  Google Scholar 

  • Greig, D.M., Porteous, B.T. and Seheult, A.H. (1989). Exact maximum a posteriori estimation for binary images. J. R. Statist. Soc. B, 51: 271–279

    Google Scholar 

  • Guyon, X. (1995). Random Fields on a Network. Modeling, Statistics, and Applications. Springer-Verlag, Berlin

    Google Scholar 

  • Hu, R. and Fahmy, M.M. (1992). Texture segmentation based on a hierarchical Markov random field model. Signal Processing, 26: 285–305

    Article  Google Scholar 

  • Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953). Equations of state calculations by fast computing machines. J. Chem. Phys., 21: 1087–1092

    Article  Google Scholar 

  • Lakshmanan, S. and Derin, H. (1989). Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. IEEE Trans. PAMI, 11: 799–813

    Article  Google Scholar 

  • Winkler, G. (1995). Image Analysis, Random Fields and Dynamic Monte Carlo Methods. Springer, Berlin

    Google Scholar 

  • Younes, L. (1989). Parametric inference for imperfectly observed Gibbsian fields. Prob. Th. Rel. Fields, 82: 625–645

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Physica-Verlag Heidelberg

About this paper

Cite this paper

Janžura, M. (1996). Image Processing, Markov Chain Approach. In: Prat, A. (eds) COMPSTAT. Physica-Verlag HD. https://doi.org/10.1007/978-3-642-46992-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-46992-3_8

  • Publisher Name: Physica-Verlag HD

  • Print ISBN: 978-3-7908-0953-4

  • Online ISBN: 978-3-642-46992-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics