Unsupervised image segmentation using gibbs sampler within a multiresolution framework

Chang Tsun Li

Research output: Book chapter/Published conference paperConference paper

Abstract

This work approaches the texture segmentation problem using Gibbs sampler (i.e., the combination of Markov random fields and simulated annealing) within a multiple resolutions framework with "high class resolution and low boundary resolution" at high levels and "low class resolution and high boundary resolution" at lower ones. As the algorithm descends the multiresolution structure, the coarse segmentation results are propagated down to the next lower level so as to reduce the inherent class-boundary uncertainty and to improve the segmentation accuracy. The under-segmentation problem due to the excessive inter-scale interaction in our previous work is addressed and a new neighborhood system and paradigm for inter-scale interaction is proposed to attack the problem.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications, EuroIMSA 2005
EditorsM.H. Hamza
Pages516-520
Number of pages5
Publication statusPublished - 2005
EventIASTED International Conference on Internet and Multimedia Systems and Applications, EuroIMSA 2005 - Grindelwald, Switzerland
Duration: 21 Feb 200523 Feb 2005

Conference

ConferenceIASTED International Conference on Internet and Multimedia Systems and Applications, EuroIMSA 2005
CountrySwitzerland
CityGrindelwald
Period21/02/0523/02/05

Fingerprint Dive into the research topics of 'Unsupervised image segmentation using gibbs sampler within a multiresolution framework'. Together they form a unique fingerprint.

  • Cite this

    Li, C. T. (2005). Unsupervised image segmentation using gibbs sampler within a multiresolution framework. In M. H. Hamza (Ed.), Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications, EuroIMSA 2005 (pp. 516-520). [462-026]