[1]程全,刘晓青,刘玉春,等.基于Mean Shift聚类的多级阈值化方法[J].郑州大学学报(工学版),2017,38(06):64-69.[doi:10.13705/j.issn.1671-6833.2017.06.009]
　Cheng Quan,Liu Xiaoqing,Liu Yuchun,et al.Based on the Mean Shift Clustering Multilevel Threshold Method[J].Journal of Zhengzhou University (Engineering Science),2017,38(06):64-69.[doi:10.13705/j.issn.1671-6833.2017.06.009]
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基于Mean Shift聚类的多级阈值化方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
38卷
期数:
2017年06期
页码:
64-69
栏目:
出版日期:
2017-11-20

文章信息/Info

Title:
Based on the Mean Shift Clustering Multilevel Threshold Method
作者:
程全; 刘晓青; 刘玉春; 王志良
1.周口师范学院机械与电气工程学院,河南周口,466001;2.北京科技大学 计算机与通信工程学院,北京,100083
Author(s):
Cheng Quan1; Liu Xiaoqing1; Liu Yuchun1; Wang Zhiliang2
1. School of Mechanical and Electrical Engineering, Zhoukou Normal University, Zhoukou, Henan, 466001, China; 2. School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083
关键词:
多级阈值化; 图像分割; 迭代阈值化; 分割质量评估
Keywords:
DOI:
10.13705/j.issn.1671-6833.2017.06.009
文献标志码:
A
摘要:
为了解决多级阈值化技术中所选阈值的数量通常不能预先确定的问题,提出一种基于Mean Shift 聚类技术的新型多级阈值化方法.首先,通过使用Mean Shift技术探寻出潜在的模式中心,应用迭代的阈值选择方法来自动确定相邻模式中心的各个阈值;然后,采用多级阈值化对图像进行分割;最后,通过实验验证了基于Mean Shift聚类技术分割的图像相对于原始图像的对比度有了很大提高.该方法通过简单修改程序参数就能够灵活控制分割精度,可以广泛应用于单阈值分割、多级阈值分割和有损压缩等技术中.
Abstract:
In order to solve the problem that the number of selected thresholds in multilevel thresholds cannot be usually predetermined,a novel multi-level thresholding method based on Mean Shift Clustering technique was proposed.Using Mean Shift technology to explore the potential mode center,the various thresholds of ad-jacent to the mode center was automatically determined by using of iterative threshold selection method, and then the method of multi-level threshold was used for image segmentation.The experimental results showed that,relative to the original image,contrast of the image split with Mean Shift clustering technique was greatly improved.This method could control the segmentation precision flexibly by simply modifying parameters of the program,and could be widely used in the technology of single threshold segmentation, multi-level threshold segmentation and detrimental compression and other technologies.

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