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ltx200

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Sheng-Jun Xu，Jiu-Qiang Han。 Image segmentation using adaptive loopy belief propagation，Optik,2013,124(22):5732-5738.

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1楼 2014-03-08 16:00:04

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ltx200: 金币+1, ★★★★★最佳答案 2014-03-08 16:35:20

http://apps.webofknowledge.com/f ... age=1&doc=1

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2楼2014-03-08 16:23:58

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ltx200: 金币+3, ★★★★★最佳答案 2014-03-08 16:35:10
gruyclewee: 金币+3, 感谢应助，奖励一下。 2014-03-08 21:11:37
oven1986: 检索EPI+1, 感谢应助，鼓励一下。 2014-03-09 23:38:44

Image segmentation using adaptive loopy belief propagation

作者:Xu, SJ (Xu, Sheng-Jun)[ 1,2 ] ; Han, JQ (Han, Jiu-Qiang)[ 1 ] ; Yu, JQ (Yu, Jun-Qi)[ 2 ] ; Zhao, L (Zhao, Liang)[ 2 ]

OPTIK

卷: 124

期: 22

页: 5732-5738

DOI: 10.1016/j.ijleo.2013.04.013

出版年: 2013

查看期刊信息



摘要

Loopy belief propagation (LBP) algorithm over pairwise-connected Markov random fields (MRFs) has become widely used for low-level vision problems. However, Pairwise MRF is often insufficient to capture the statistics of natural images well, and LBP is still extremely slow for application on an MRF with large discrete label space. To solve these problems, the present study proposes a new segmentation algorithm based on adaptive LBP. The proposed algorithm utilizes local region information to construct a local region model, as well as a local interaction region MRF model for image segmentation. The adaptive LBP algorithm maximizes the global probability of the proposed MRF model, which employs two very important strategies, namely, "message self-convergence" and "adaptive label pruning". Message self-convergence can improve the reliability of a pixel in choosing a label in local region, and label pruning can dismiss impossible labels for every pixel. Thus, the most reliable information messages transfer through the LBP algorithm. The experimental results show that the proposed algorithm not only obtains more accurate segmentation results but also greater speed. (C) 2013 Elsevier GmbH. All rights reserved.

关键词

作者关键词:Adaptive loopy belief propagation; Markov random fields; Local interaction region MRF; Message self-convergence; Label pruning

KeyWords Plus:MARKOV RANDOM-FIELDS; ENERGY MINIMIZATION; GRAPH CUTS; VISION

作者信息

通讯作者地址: Xu, SJ (通讯作者)

Xi An Jiao Tong Univ, MoE Key Lab Intelligent Networks & Network Secur, Xian 710049, Peoples R China.

地址:

[ 1 ] Xi An Jiao Tong Univ, MoE Key Lab Intelligent Networks & Network Secur, Xian 710049, Peoples R China

[ 2 ] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian 710055, Peoples R China

电子邮件地址:duplin@sina.com

基金资助致谢

基金资助机构

授权号

National Natural Science Foundation for Young Scientists of China
51209167

Natural Science Foundation of Shaanxi Province, China
2012JM8026

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出版商

ELSEVIER GMBH, URBAN & FISCHER VERLAG, OFFICE JENA, P O BOX 100537, 07705 JENA, GERMANY

类别 / 分类

研究方向:Optics

Web of Science 类别:Optics

文献信息

文献类型:Article

语种:English

入藏号: WOS:000325835400098

ISSN: 0030-4026

期刊信息

Impact Factor (影响因子):  Journal Citation Reports®

其他信息

IDS 号: 236YM

Web of Science 核心合集中的 "引用的参考文献": 27

Web of Science 核心合集中的 "被引频次":

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3楼2014-03-08 16:24:17

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