基于分布信息直觉模糊c均值聚类的红外图像分割算法

王晓飞; 胡凡奎; 黄硕

doi:10.11959/j.issn.1000-436x.2020071

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基于分布信息直觉模糊c均值聚类的红外图像分割算法

学术论文 | 更新时间：2024-06-05

- 基于分布信息直觉模糊c均值聚类的红外图像分割算法
- Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering
- 通信学报 2020年41卷第5期页码：120-129
- 作者机构：
  
  黑龙江大学电子工程学院，黑龙江哈尔滨 150080
- 作者简介：
  
  [ "王晓飞（1977- ），男，黑龙江哈尔滨人，博士，黑龙江大学副教授，主要研究方向为高光谱数据分析和图像处理、多源信息融合、模式识别和分类" ]
  [ "胡凡奎（1996- ），男，山东济宁人，黑龙江大学硕士生，主要研究方向为遥感图像处理" ]
  [ "黄硕（1994- ），男，黑龙江哈尔滨人，黑龙江大学硕士生，主要研究方向为遥感图像处理" ]
- 基金信息：
  
  国家自然科学基金资助项目(61871150);国家重点研发计划基金资助项目(2016YFB0502502)
- DOI：10.11959/j.issn.1000-436x.2020071
  中图分类号： TP391.41
- 网络出版日期：2020-05，
  
  纸质出版日期：2020-05-25
- 稿件说明：
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王晓飞, 胡凡奎, 黄硕. 基于分布信息直觉模糊c均值聚类的红外图像分割算法[J]. 通信学报, 2020,41(5):120-129.

Xiaofei WANG, Fankui HU, Shuo HUANG. Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering[J]. Journal on communications, 2020, 41(5): 120-129.
王晓飞, 胡凡奎, 黄硕. 基于分布信息直觉模糊c均值聚类的红外图像分割算法[J]. 通信学报, 2020,41(5):120-129. DOI： 10.11959/j.issn.1000-436x.2020071.

Xiaofei WANG, Fankui HU, Shuo HUANG. Infrared image segmentation algorithm based on distribution information intuitionistic fuzzy c-means clustering[J]. Journal on communications, 2020, 41(5): 120-129. DOI： 10.11959/j.issn.1000-436x.2020071.

摘要

针对传统的直觉模糊c均值聚类算法进行图像分割时对聚类中心敏感导致最终聚类精度低、细节保留性差、时间复杂度较大等不足，提出了一种适用于电力设备红外图像分割的基于分布信息的直觉模糊c均值聚类算法。红外图像中高强度的非目标对象与图像强度不均匀对图像分割有较强干扰，所提算法能有效抑制该干扰。首先，将高斯模型引入电力设备的全局空间分布信息中以改进IFCM算法；其次，利用局部空间信息的空间算子优化隶属函数来解决边缘模糊和图像强度不均匀问题。经过对Terravic动态红外数据库与包含300 幅电力设备红外图像的数据集进行实验，相对区域错误率在10%左右，受模糊因子m变化影响较小，验证了所提算法在有效性与适用性上明显优于其他对比算法。

Abstract

Due to the sensitivity of the traditional intuitionistic fuzzy c-means (IFCM) clustering algorithm to the clustering center in image segmentation

which resulted in the low clustering precision

poor retention of details

and large time complexity

an intuitionistic fuzzy c-means clustering algorithm was proposed based on spatial distribution information suitable for infrared image segmentation of power equipment.The non-target objects with high intensity and the non-uniformity of image intensity in the infrared image had strong interference to the image segmentation

which could be effectively suppressed by the proposed algorithm.Firstly

the Gaussian model was introduced into the global spatial distribution information of power equipment to improve the IFCM algorithm.Secondly

the membership function was optimized by local spatial operator to solve the problem of edge blur and image intensity inhomogeneity.The experiments conducted on Terravic motion IR database and the data set containing 300 infrared images of power equipment show that

the relative region error rate is about 10% and is less affected by the change of fuzzy factor m.The effectiveness and applicability of the proposed algorithm are superior to other comparison algorithms.

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references

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