An Otsu Image Segmentation Algorithm Based on Chaos Optimization of Two BSA

WU Jun; WANG Long-long

Chinese

您当前的位置：

首页 >

文章列表页 >

An Otsu Image Segmentation Algorithm Based on Chaos Optimization of Two BSA

更新时间：2026-09-04

- An Otsu Image Segmentation Algorithm Based on Chaos Optimization of Two BSA
- Vol. 35, Issue 12, Pages: 119-124(2018)
- 作者机构：
  
  江西理工大学机构 , 信息工程学院,江西,赣州,341000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
移动端阅览
吴军, 王龙龙. 基于双鸟群混沌优化的otsu图像分割算法[J]. 微电子学与计算机, 2018, 35(12): 119-124.

WU Jun, WANG Long-long. An Otsu Image Segmentation Algorithm Based on Chaos Optimization of Two BSA[J]. 2018, 35(12): 119-124.
吴军, 王龙龙. 基于双鸟群混沌优化的otsu图像分割算法[J]. 微电子学与计算机, 2018, 35(12): 119-124. DOI：

WU Jun, WANG Long-long. An Otsu Image Segmentation Algorithm Based on Chaos Optimization of Two BSA[J]. 2018, 35(12): 119-124. DOI：

摘要

为了提高鸟群图像分割算法在求取图像最佳阈值时的准确性与稳定性

提出了一种双鸟群混沌优化图像分割算法.在求取图像二维otsu阈值时对两个种群求得的二维解进行交叉互换

并加入混沌扰动

加强了搜索能力.为了减少噪声对图像分割结果的影响

使用一种改进的自适应选择中值-均值滤波法对图像进行预处理

增强了图像分割时对噪声的鲁棒性.实验结果表明双鸟群混沌优化图像分割算法在求取一维、二维otsu阈值时的准确度和稳定性要优于其他算法

且无论在低噪声污染还是高噪声污染下都能对图像进行较为准确的分割.

Abstract

In order to improve the accuracy and stability of bird swarm image segmentation algorithm in finding the optimal threshold of image

a two-bird swarm chaos optimization image segmentation algorithm is proposed. The two-dimensional solutions obtained by two populations are cross-exchanged when the image two-dimensional otsu threshold is calculated

chaos disturbances are added to enhance the search capability. In order to reduce the influence of noise on the image segmentation results

an improved adaptive selection median-mean filter method is used to preprocess the image

which enhances the robustness to noise during image segmentation. Experimental results show that the two-bird swarm chaos-optimized image segmentation algorithm is superior to other algorithms in obtaining the one-dimensional and two-dimensional otsu thresholds

The image can be accurately segmented regardless of low noise pollution or high noise pollution.

关键词

Keywords

references

Zhao J H, Wang X, Zhang H M, et al.Side scan sonar image segmentation based on neutrosophic set and quantum-behaved particle swarm optimization algorithm[J].Marine Geophysical Research, 2016, 37(3):229-241. DOI: 229

何海明, 齐冬莲, 张国月, 等.快速高效去除图像椒盐噪声的均值滤波算法[J].激光与红外, 2014, 44(4):469-472. DOI: 469

Li Zhang, KaiTeng Wu, Tao Zhang.Retinal Vessel Segmentation Combined Two-Dimensional Entropy Method and Double Populations Genetic Algorithm[J].International Journal of Pattern Recognition and Artificial Intelligence, 2017, 31(5):1-13. DOI: 1

P Jedlicka, T Ryba.Genetic algorithm application in image segmentation[J].Pattern Recognition and Image Analysis, 2016, 26(3):497-501. DOI: 497

Zou G H.Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain Images[J].International Journal Bioautomation, 2016, 20(4):505-514. DOI: 505

牛培峰, 王丘亚, 马云鹏, 等.基于量子自适应鸟群算法的锅炉NOx排放特性研究[J].计量学报, 2017, 38(6):770-775. DOI: 770

曾艳阳, 冯云霞, 赵文涛.基于logistic映射的自适应变尺度混沌粒子群算法[J].系统仿真学报, 2017, 29(10):2241-2246. DOI: 2241

Li Haiyang, He Hongzhou, Wen Yongge.Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation[J].Optik - International Journal for Light and Electron Optics, 2015, 126(24):4817-4822. DOI: 4817

Mohammad Taherdangkoo, Mohammad Hadi Bagheri, Mehran Yazdi, et al.An Effective Method for Segmentation of MR Brain Images Using the Ant Colony Optimization Algorithm[J].Journal of Digital Imaging, 2013, 26(6):1116-1123. DOI: 1116

Gui Y F, Su P, Chen X Q.Image Segmentation based on Genetic Algorithms and Grey Relational Analysis[J].Journal of Grey System, 2016, 28(1):45-51. DOI: 45

曾嶒, 彭春华, 王奎, 等.基于鸟群算法的微电网多目标运行优化[J].电力系统保护与控制, 2016, 44(13):117-122. DOI: 117

覃晓, 梁伟, 元昌安, 等.基于遗传优化谱聚类的图形分割方法[J].计算机科学, 2017, 44(1):100-102. DOI: 100

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on automatic hair segmentation based on machine learning

Post Segmentation of Fuzzy Clustering Algorithm

Threshold Image Segmentation Method Based on Cloud Genetic Algorithm

Image Segment Based on Improved Active Contour Model

Image Segmentation Based on the Region of Color Space and LBP Algorithm

Related Author

CHEN Rongyang

LI Jianwei

LIN Xia

WANG Yuan

ZHENG Yi-bo

ZHU Zhan-long

DU Yong-gui

LI Ting

Related Institution

School of Information Engineering, Heibei GEO University

Hebei Key Laboratory of Optoelectronic Information and Geo-Detection Technology, Heibei GEO University

College of Information Engineering, Taiyuan University of Technology

Department of Information Engineering, Zhengzhou Shengda College of Economics & Trade Management

School of Computer and Control Engineering, North University of China

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰