PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Nonparametric Bayesian Image Segmentation
Peter Orbanz and Joachim Buhmann
(2005) Technical Report. Department of Computer Science, ETH Zurich, Switzerland.

Abstract

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. A nonparametric Bayesian model for histogram clustering is proposed which automatically determines the number of segments when spatial smoothness constraints on the class assignments are enforced by a Markov Random Field. A Dirichlet process prior controls the number of clusters. The resulting posterior is efficiently sampled by a variant of a conjugate-case sampling algorithm for Dirichlet process mixture models. Experimental results are provided for real-world gray value images, synthetic aperture radar images and magnetic resonance imaging data.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Theory & Algorithms
ID Code:2000
Deposited By:Peter Orbanz
Deposited On:14 January 2006