Adaptive segmentation of images of objects with smooth surfaces

George K. Gregoriou, Amir Waks, Oleh J. Tretiak

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We address the problem of adaptive segmentation of images of objects with smooth surfaces. The images are composed of regions of slowly varying intensities that may be corrupted by additive noise. The underlying field is modelled by a Markov random field that consists of both a label process which contains the classification of each pixel in the image and intensity functions which contain the possible grey levels that each pixel may take. The algorithm iteratively repeats two steps; (a) the parameter estimation step, in which the ML estimates of the associated parameters are obtained, and (b) the restoration step, in which the underlying field is estimated through the MAP method. The major contribution of this paper is the idea of allowing the pixel grey values to vary across the image regions. These values are estimated by using windows on the observed data and, as the algorithm progresses, the window size is decreased so that the algorithm adapts to the characteristics of each region.

Original languageEnglish
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages772-773
Number of pages2
ISBN (Print)0818638826
Publication statusPublished - 1993
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: 15 Jun 199318 Jun 1993

Other

OtherProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period15/06/9318/06/93

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