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A class of discrete multiresolution random fields and its application to image segmentation
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.
History
Journal
IEEE transactions on pattern analysis and machine intelligenceVolume
25Issue
1Pagination
42 - 56Publisher
IEEELocation
Piscataway, N.J.Publisher DOI
ISSN
0162-8828Language
engPublication classification
C1.1 Refereed article in a scholarly journalCopyright notice
2003, IEEEUsage metrics
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Keywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicComputer ScienceEngineeringMarkov random fieldsimage segmentationBayesian estimationMARKOV RANDOM-FIELDUNSUPERVISED TEXTURE SEGMENTATIONSTATISTICAL-ANALYSISBOUNDARY DETECTIONMODELSREPRESENTATIONSRECONSTRUCTIONCLASSIFICATIONInformation SystemsArtificial Intelligence and Image Processing
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