A class of discrete multiresolution random fields and its application to image segmentation

Roland Wilson, Chang Tsun Li

Research output: Contribution to journalArticle

67 Citations (Scopus)

Abstract

In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.

Original languageEnglish
Pages (from-to)42-56
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume25
Issue number1
DOIs
Publication statusPublished - Jan 2003

Fingerprint Dive into the research topics of 'A class of discrete multiresolution random fields and its application to image segmentation'. Together they form a unique fingerprint.

  • Cite this