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Proceedings Paper

New stochastic sampling method for region extraction: theory and experiments
Author(s): Taizo Anan; Makoto Ohtsu; Hiroyuki Kudo; Tsuneo Saito
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Paper Abstract

We propose a region extraction method based on a new energy function and a new stochastic sampling method. The new energy function is based on the mixed density description derived by clustering an input image using the ISO DATA algorithm. Our energy function is suitable for natural images. We developed a new stochastic sampling method by modifying the conventional Gibbs sampler. The conventional Gibbs sampler converges to global optimum of the energy function, but is cannot be applied to region extraction because of its inability to preserve topological property of the initial region during its state transition process. To overcome this drawback, our sampling process is driven by 'dynamic site selection' which enables to preserve the topology of the initial region in the state transition process. We prove the global convergence property of our proposed sampling method by extending the existing stochastic sampling theories. We demonstrate the performances of our method by simulation studies for both synthetic and natural images.

Paper Details

Date Published: 8 October 1996
PDF: 10 pages
Proc. SPIE 2823, Statistical and Stochastic Methods for Image Processing, (8 October 1996); doi: 10.1117/12.253443
Show Author Affiliations
Taizo Anan, Univ. of Tsukuba (Japan)
Makoto Ohtsu, Univ. of Tsukuba (Japan)
Hiroyuki Kudo, Univ. of Tsukuba (Japan)
Tsuneo Saito, Univ. of Tsukuba (Japan)

Published in SPIE Proceedings Vol. 2823:
Statistical and Stochastic Methods for Image Processing
Edward R. Dougherty; Francoise J. Preteux; Jennifer L. Davidson, Editor(s)

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