An adaptive weighting parameter estimation between local and global intensity fitting energy for image segmentation

被引：7
|
作者：
Wang, Hui [1 ,2 ]
Huang, Ting-Zhu [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Inst Computat Sci, Chengdu 611731, Sichuan, Peoples R China
[2] Anshun Univ, Dept Math & Comp Sci, Anshun 561000, Guizhou, Peoples R China
来源：
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2014年 / 19卷 / 09期
关键词：
Image segmentation; Intensity inhomogeneity; Level set method; Chan-Vese model; LBF model; ACTIVE CONTOURS; EVOLUTION; MUMFORD;
D O I：
10.1016/j.cnsns.2014.02.015
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
Local and global intensity fitting energy are widely used for image segmentation. In order to improve the segmentation quality in the presence of intensity inhomogeneity, in this paper, we propose a new adaptive rule for obtaining weighting parameter estimation between the local and global intensity fitting energy. Following the minimization of the energy functional, the value of the weighting parameter is dynamically updated with the contour evolution, which is effective and accurate for extracting the object. (C) 2014 Elsevier B. V. All rights reserved.
引用
收藏
页码：3098 / 3105
页数：8
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