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Nonparametric Bayesian Image Segmentation

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Abstract

Image segmentation algorithms partition the set of pixels of an image into a specific number of different, spatially homogeneous groups. We propose a nonparametric Bayesian model for histogram clustering 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 level of resolution which corresponds to the number of clusters in data with a unique cluster structure. 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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Authors and Affiliations

  1. Institute of Computational Science, ETH Zürich, Universitaet-Strasse 6, ETH Zentrum, CAB G 84.1, Zurich, 8092, Switzerland

    Peter Orbanz & Joachim M. Buhmann

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  1. Peter Orbanz
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  2. Joachim M. Buhmann
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Correspondence to Peter Orbanz.

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Orbanz, P., Buhmann, J.M. Nonparametric Bayesian Image Segmentation. Int J Comput Vis 77, 25–45 (2008). https://doi.org/10.1007/s11263-007-0061-0

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  • DOI: https://doi.org/10.1007/s11263-007-0061-0

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