Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17280
Title: A MAP estimation based segmentation model for speckled images
Authors: Han, Y
Baciu, G 
Xu, C
Keywords: Alternative direction iteration
Chambolle's projection
MAP
Speckled image
Split Bregman
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings of 2014 International Conference on Smart Computing, SMARTCOMP 2014, 2014, 7043836, p. 35-41 How to cite?
Abstract: In this paper, we propose a new fuzzy-based variational model that efficiently computes partitioning of speckled images, such as images obtained from Synthetic Aperture Radar (SAR). The model is derived by using the so-called maximizing a posteriori (MAP) estimation method. The novelties of the model are: (1) the Gamma distribution rather than the classical Gaussian distribution is used to model the gray intensities in each homogeneous region of the images (Gamma distribution function is better suited for speckled images); (2) an adaptive weighted regularization term with respect to a fuzzy membership function is designed to protect the segmentation results from degeneration (being over-smoothed). Compared with the classical total variation (TV) regularizer, the proposed regularization term has a sparser property. In addition, a new alternative direction iteration algorithm is proposed to solve the model. The algorithm is efficient since it integrates the split Bregman method and the Chambolle's projection method. Numerical examples are given to verify the efficiency of our model.
Description: 2014 1st International Conference on Smart Computing, SMARTCOMP 2014, Hong Kong, 3-5 November 2014
URI: http://hdl.handle.net/10397/17280
ISBN: 9781479957125
DOI: 10.1109/SMARTCOMP.2014.7043836
Appears in Collections:Conference Paper

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