[1]马英辉,吴一全.利用混沌布谷鸟优化的二维Renyi灰度熵图像阈值选取[J].智能系统学报,2018,13(01):152-158.[doi:10.11992/tis.201607004]
 MA Yinghui,WU Yiquan.Two-dimensional Renyi-gray-entropy image threshold selection based on chaotic cuckoo search optimization[J].CAAI Transactions on Intelligent Systems,2018,13(01):152-158.[doi:10.11992/tis.201607004]
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利用混沌布谷鸟优化的二维Renyi灰度熵图像阈值选取(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年01期
页码:
152-158
栏目:
出版日期:
2018-01-24

文章信息/Info

Title:
Two-dimensional Renyi-gray-entropy image threshold selection based on chaotic cuckoo search optimization
作者:
马英辉12 吴一全1345
1. 南京航空航天大学 电子信息工程学院, 江苏 南京 211106;
2. 宿迁学院 信息工程学院, 江苏 宿迁 223800;
3. 西华大学 制造与自动化省高校重点实验室, 四川 成都 610039;
4. 华中科技大学 数字制造装备与技术国家重点实验室, 湖北 武汉 430074;
5. 安徽理工大学 煤矿安全高效开采省部共建教育部重点实验室, 安徽 淮南 232001
Author(s):
MA Yinghui12 WU Yiquan1345
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. School of Information Engineering, Suqian College, Suqian 223800, China;
3. Key Laboratory of Manufacturing & Automat
关键词:
图像分割阈值选取布谷鸟算法Renyi灰度熵灰度-梯度二维直方图混沌优化Arimoto熵Tsallis灰度熵
Keywords:
image segmentationthreshold selectioncuckoo search algorithmRenyi gray entropygray-gradient two-dimensional histogramchaotic optimizationArimoto entropyTsallis gray entropy
分类号:
TP391.41
DOI:
10.11992/tis.201607004
摘要:
为了进一步降低现有的Renyi熵阈值法的计算复杂度,提出了基于混沌布谷鸟算法和二维Renyi灰度熵的阈值选取。首先,引入一维Renyi灰度熵阈值选取公式,建立基于像素灰度和邻域梯度的二维直方图,推导出基于该直方图的二维Renyi灰度熵阈值选取公式,通过快速递推公式来减少阈值准则函数的计算量;最后,采用混沌布谷鸟算法搜索最优阈值来完成图像分割。结果表明,与二维Arimoto熵法、基于粒子群的二维Renyi熵法、基于混沌粒子群的二维Tsallis灰度熵法、基于布谷鸟算法的二维Renyi灰度熵法相比,所提出的方法能够准确实现图像分割,且运算速度有所提升。
Abstract:
To further reduce the computational complexity of existing thresholding methods based on Renyi’s entropy, in this paper, we propose a method for threshold selection based on 2-D Renyi-gray-entropy image threshold selection and chaotic cuckoo search optimization. First, we derive the formula for a 1-D Renyi-gray-entropy threshold selection. Then, we build a 2-D histogram based on the grayscale and gray-gradient and derive a formula for 2-D Renyi-gray-entropy threshold selection based on this histogram. We use fast recursive algorithms to eliminate redundant computation in the threshold-selection criterion function. Finally, to achieve image segmentation, we search for the optimal threshold using the chaotic cuckoo search algorithm. The experimental results show that, compared with 2-D Arimoto-entropy thresholding method, the 2-D Renyi-entropy thresholding method based on particle swarm optimization, the 2-D Tsallis-gray-entropy thresholding method using chaotic particle swarm, and the 2-D Renyi-gray-entropy thresholding method based on the cuckoo search, our proposed method can segment objects more accurately and has a higher running speed.

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备注/Memo

备注/Memo:
收稿日期:2016-07-05。
基金项目:西华大学制造与自动化省高校重点实验室开放课题(S2jj2014-028);华中科技大学数字制造装备与技术国家重点实验室开放课题(DMETKF2014010);安徽理工大学煤矿安全高效开采省部共建教育部重点实验室开放课题(JYBSYS2014102).
作者简介:马英辉,女,1979年生,讲师,硕士研究生,主要研究方向为图像处理与分析、图像处理与视频通信。发表学术论文7篇;吴一全,男,1963年生,教授,博士生导师,博士,主要研究方向为图像处理与分析、目标检测与识别、智能信息处理。发表学术论文280余篇,被SCI、EI检索160余篇。
通讯作者:吴一全.Email:nuaaimage@163.com.
更新日期/Last Update: 2018-02-01