Paper Info
Title
A variational based smart segmentation model for speckled images.
Abstract
In this paper, we propose a new variational model in the fuzzy framework to achieve the task of partitioning speckled images, such as the synthetic aperture radar (SAR) images. The model is partly derived by using the so-called maximizing a posteriori (MAP) estimation method. The novelties of the model are that (1) the Gamma distribution rather than the classical Gaussian distribution is used to simulate the gray intensities in each homogeneous region of the images; (2) a smart regularization term with respect to fuzzy membership functions is designed. The newly designed regularization term equals to an adaptive weighted total variation (TV) regularizer. Compared with the classical TV regularizer, the proposed regularization term not only has a sparser property, but also protects the segmentation results from degeneration (being over-smoothed). In addition, a new algorithm based on the alternative direction iteration algorithm is proposed to solve the model. The algorithm is efficient since it integrates the split Bregman method and Chambolle's projection method. Numerical examples are given to verify the promising efficiency of our model. (C) 2015 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.neucom.2015.07.115
Neurocomputing
Keywords
Field
DocType
ACTIVE CONTOURS DRIVEN,LEVEL SET METHOD,BIAS CORRECTION,MINIMIZATION,ALGORITHM,DISTANCE
Pattern recognition,Synthetic aperture radar,Segmentation,A priori and a posteriori,Fuzzy logic,Projection method,Regularization (mathematics),Bregman method,Gaussian,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
178
C
0925-2312
Citations 
PageRank 
References 
2
0.36
21
Authors
3
Name
Order
Citations
PageRank
Yu Han11148.61
Chen Xu226929.36
George Baciu340956.17