An adaptive approach for texture modelling

M. T. Johnson, M. D. Desai

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Markov random fields (MRFs) continue to be an important and useful representation for modelling textured images. Standard methods for MRF image modeling make use of the equivalent Gibbs distribution (GD) to express the joint probabilities of groups of neighboring pixels. The authors investigate a new approach to the use of the GD in image modeling. Specifically, they develop an adaptive approach to the formation of clique potential functions for the distribution. Traditional tools, such as the multi-level logistic (MLL) model, have been based on the use of a predetermined and identical set of potential functions. In the present paper it is shown that by incorporating additional parameters into the model in order to control the shape of these functions, it is possible to arrive at a more complete parametrization of the image. A simple model based on this concept is described and implemented, and image simulations using the well-known Gibbs sampler algorithm are constructed to demonstrate the usefulness of an adaptive set of potential functions.

Original languageEnglish
Article number413821
Pages (from-to)388-391
Number of pages4
JournalProceedings - International Conference on Image Processing, ICIP
StatePublished - 1994
EventThe 1994 1st IEEE International Conference on Image Processing - Austin, TX, USA
Duration: Nov 13 1994Nov 16 1994

Bibliographical note

Publisher Copyright:
© 1994 IEEE.

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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