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Proceedings Paper

Image segmentation based on multiscale random field models
Author(s): Ahmet Mufit Ferman; Erdal Panayirci
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Paper Abstract

Recently a new approach to Bayesian image segmentation has been proposed by Bouman and Shapiro, based on a multiscale random field (MSRF) model along with a sequential MAP (SMAP) estimator as an efficient and computationally feasible alternative to MAP segmentation. But their method is restricted to image models with observed pixels that are conditionally independent given their class labels. In this paper, we follow the approach of and extend the SMAP method for a more general class of random field models. The proposed scheme is recursive, yields the exact MAP estimate, and is readily applicable to a broad range of image models. We present simulations on synthetic images and conclude that the generalized algorithm performs better and requires much less computation than maximum likelihood segmentation.

Paper Details

Date Published: 27 February 1996
PDF: 11 pages
Proc. SPIE 2727, Visual Communications and Image Processing '96, (27 February 1996); doi: 10.1117/12.233284
Show Author Affiliations
Ahmet Mufit Ferman, Istanbul Technical Univ. (United States)
Erdal Panayirci, Istanbul Technical Univ. (Turkey)


Published in SPIE Proceedings Vol. 2727:
Visual Communications and Image Processing '96
Rashid Ansari; Mark J. T. Smith, Editor(s)

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