[1]苏洪超,胡英,洪少壮.基于红外图像特征与K-means的边缘检测[J].红外技术,2020,42(1):081-85.[doi:10.11846/j.issn.1001_8891.202001012]
 SU Hongchao,HU Ying,HONG Shaozhuang.Edge Detection Based on Characteristics of Infrared Image and K-means[J].Infrared Technology,2020,42(1):081-85.[doi:10.11846/j.issn.1001_8891.202001012]
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基于红外图像特征与K-means的边缘检测
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第1期
页码:
081-85
栏目:
出版日期:
2020-01-23

文章信息/Info

Title:
Edge Detection Based on Characteristics of Infrared Image and K-means
文章编号:
1001-8891(2020)01-0081-05
作者:
苏洪超胡英洪少壮
大连海事大学 船舶电气工程学院
Author(s):
SU HongchaoHU YingHONG Shaozhuang
School of Marine Electrical Engineering, Dalian Maritime University
关键词:
红外图像边缘检测弱边缘K均值聚类
Keywords:
infrared imagery edge detection weak edge K-means
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.202001012
文献标志码:
A
摘要:
为解决红外图像边缘模糊导致边缘提取困难的问题,提出一种基于边缘特征与K-means结合的红外图像边缘检测方法。首先将人眼视觉特性与红外图像边缘点处的灰度分布特点结合,构造出反映其结构特征的数据集;再利用K-means将数据集分为边缘点和非边缘点,提取出图像边缘;最后利用二步法将边缘进行细化,以便实现红外图像边缘检测。实验结果表明:该方法能够通过自适应阈值提取出红外图像的完整外部轮廓,并保留内部边缘信息,对弱边缘起到良好的提取效果,并有效抑制噪声干扰。
Abstract:
An infrared image edge detection method based on edge characteristics combined with K-means is proposed in this study to solve the problem of edge extraction caused by the blurring of infrared image edges. First, human visual characteristics are combined with gray distribution characteristics at the edge of the infrared image to construct a data set reflecting its structural characteristics. Second, K-means is used to classify the data set into edge and non-edge points to extract the image edges. Third, the edge is refined using a two-step method to achieve infrared image edge detection. The experimental results show that the proposed method can extract the complete external contour of the infrared image through the adaptive threshold and retain the internal edge information, which can extract the weak edge and effectively suppress noise interference.

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备注/Memo

备注/Memo:
收稿日期:2019-07-08;修订日期:2019-12-24.
作者简介:苏洪超(1996-),男,硕士研究生,研究方向为红外图像边缘提取、模式识别。E-mail:hongchaosuvip@163.com。

更新日期/Last Update: 2020-01-20