[1]汪方斌,孙凡,王峰,等.基于Tsallis熵的红外偏振热像分割算法[J].红外技术,2020,42(3):245-256.[doi:10.11846/j.issn.1001_8891.202003007]
 WANG Fangbin,SUN Fan,WANG Feng,et al.Infrared Polarization Thermal Image Segmentation Algorithm Based on Tsallis Entropy[J].Infrared Technology,2020,42(3):245-256.[doi:10.11846/j.issn.1001_8891.202003007]
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基于Tsallis熵的红外偏振热像分割算法
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
42卷
期数:
2020年第3期
页码:
245-256
栏目:
出版日期:
2020-03-23

文章信息/Info

Title:
Infrared Polarization Thermal Image Segmentation Algorithm Based on Tsallis Entropy

文章编号:
1001-8891(2020)05-0245-12
作者:
汪方斌12孙凡12王峰3赵汝海12雷经发12王雪12
1. 安徽建筑大学 机械与电气工程学院;
2. 安徽建筑大学 建筑机械故障诊断与预警重点实验室;
3. 偏振光成像探测技术安徽省重点实验室

Author(s):
WANG Fangbin12SUN Fan12WANG Feng3ZHAO Ruhai12LEI Jingfa12WANG Xue12
1. School of Mechanical and Electrical Engineering, Anhui Jianzhu University;
2. Key Laboratory of Construction Machinery Fault Diagnosis and Early Warning Technology of Anhui Jianzhu University;
3. Key Laboratory of Polarization Imaging Detection Technology in Anhui Province

关键词:
阈值分割Tsallis熵红外偏振热像
Keywords:
threshold segmentation Tsallis entropy infrared polarization thermal image
分类号:
TP391.4
DOI:
10.11846/j.issn.1001_8891.202003007
文献标志码:
A
摘要:
传统红外热像存在对比度低、边缘模糊等不足而使目标区域分割困难,红外偏振热像能够凸显边缘和轮廓特征,因此在环境监测、军事侦察、工业无损检测等领域得到广泛的应用,但如何进行红外偏振热像分割目前研究较少。为此,本文提出了一种基于Tsallis熵的红外偏振热像分割算法。首先通过Tsallis阈值对偏振方位角热像进行初分割,然后以最小化初分割热像交集与并集误差率优化Tsallis指数,再利用指数优化后的Tsallis阈值对偏振方位角热像进行优化分割并通过连通域检测去除误分割得到二次分割图,最后以二次分割图交集区域为种子区域、并集区域为边界,通过区域生长法得到最终分割热像。实验结果显示,本文算法相对最小Tsallis交叉熵法、Otsu法和模糊聚类法错分区域小,在主观视觉效果和区域间对比度、形状测度评价指标上有较大的改善,能够更准确地分割出目标。
Abstract:
There are some deficiencies in traditional infrared thermal images, such as low contrast and blurred edges, making segmentation of the target region difficult. Infrared polarization thermal imaging can clearly highlight the edges and contours of the observed objects. It has been effectively applied in environmental monitoring, military reconnaissance, industrial nondestructive testing, and other fields. However, there are few studies on infrared polarization thermal image segmentation. In this work, a novel infrared polarization thermal image segmentation algorithm based on Tsallis entropy is proposed. First, the polarization azimuth thermal image is segmented with a Tsallis threshold. Second, the Tsallis index is optimized by minimizing the intersection and union error rate of the initial segmentation thermal image. Subsequently, the polarization azimuth thermal image is segmented by utilizing the exponentially optimized Tsallis threshold, and the quadratic segmentation image is obtained by moving false segmentation through connected domain detection. Finally, taking the intersection area as the seed region and taking the union region as the boundary, the final segmentation thermal image is obtained through the regional growth method. The experimental results show that the algorithm proposed can diminish the false segmentation region. It can improve the evaluation index of the subjective visual effect, interregional contrast, and shape measurement and segment the target accurately compared with the minimum Tsallis cross-entropy, Otsu, and fuzzy clustering methods.

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备注/Memo

备注/Memo:
收稿日期:2019-07-12;修订日期:2020-02-25.
作者简介:汪方斌(1972-),男,安徽桐城人,博士,副教授,主要从事光学偏振检测、偏智能系统与模式识别、结构损伤与故障诊断等方面的研究。E-mail:993882157@qq.com。
基金项目:国家自然科学基金(61871002,51805003)、安徽省自然科学基金(1808085ME125)、安徽省教育厅高校自然科学和社会科学研究项目(KJ2017ZD42)、偏振光成像探测技术安徽省重点实验室开放课题、安徽省重点研究与开发计划项目(1804a09020009)。

更新日期/Last Update: 2020-03-17