Infrared object segmentation based on fuzzy enhancement and mean shift

doi:10.3778/j.issn.1002-8331.2010.30.008

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (30): 26-29.DOI: 10.3778/j.issn.1002-8331.2010.30.008

• 博士论坛 • Previous Articles Next Articles

Infrared object segmentation based on fuzzy enhancement and mean shift

ZHANG Kun-hua^1，2，ZHANG Li^1，2，YANG Xuan³

1.College of Information，Shenzhen University，Shenzhen，Guangdong 518060，China
2.State Key Lab on Theory and Chief Technology of Integrated Services Networks，Xi’an 710126，China
3.College of Computer and Software，Shenzhen University，Shenzhen，Guangdong 518060，China

Received:2010-05-10 Revised:2010-09-10 Online:2010-10-21 Published:2010-10-21
Contact: ZHANG Kun-hua

应用模糊增强及均值漂移实现红外目标分割

张坤华^1，2，张力^1，2，杨烜³

1.深圳大学信息工程学院，广东深圳 518060
2.综合业务网国家重点实验室，西安 710126
3.深圳大学计算机与软件学院，广东深圳 518060

通讯作者: 张坤华

Abstract

Abstract: Infrared images always have low SNR and contrast，and boundaries of infrared target are blurry.Therefore the segmentation of infrared target in complex environment is very difficult.A new algorithm for infrared target segmentation based on fuzzy enhancement and mean shift is proposed in this paper.Firstly，a new membership function of fuzzy is defined，the contrast between target and background in infrared image is improved efficiently by the enhancement method based on the fuzzy set theory，in which the disadvantages of traditional fuzzy enhancement methods are avoided.Then，the intersection of confidence intervals（ICI） rule is used to determine the bandwidth in mean shift，and a new adaptive bandwidth mean shift algorithm is presented to realize further smoothing and clustering of image.Finally，infrared target is segmented by the adaptive threshold.Experimental results indicate that the algorithm can segment the infrared target under complex environment correctly and efficiently，and the good details of target are reserved.

Key words: fuzzy set, image enhancement, mean shift, infrared target, target segmentation

摘要： 针对复杂环境下红外图像信噪比和对比度低，边缘模糊，目标分割困难的情况，提出一种基于模糊增强和均值漂移图像滤波的红外目标分割方法。首先定义新的隶属度函数，运用模糊集理论进行红外图像增强，避免了传统模糊增强算法的弊病，有效提高目标与背景的对比度；之后利用ICI（交叉置信区）规则确定均值漂移的带宽参数，提出一种新的自适应带宽均值漂移图像滤波方法，实现图像的进一步平滑和聚类；最后利用自适应阈值实现红外目标分割。实验结果表明，算法能够正确有效地分割出复杂环境下的红外目标，并且很好地保持了目标的轮廓细节。

关键词: 模糊集, 图像增强, 均值漂移, 红外目标, 目标分割

CLC Number:

TP391

ZHANG Kun-hua^1，2，ZHANG Li^1，2，YANG Xuan³. Infrared object segmentation based on fuzzy enhancement and mean shift[J]. Computer Engineering and Applications, 2010, 46(30): 26-29.

张坤华^1，2，张力^1，2，杨烜³. 应用模糊增强及均值漂移实现红外目标分割[J]. 计算机工程与应用, 2010, 46(30): 26-29.

[1]	TANG Zilin, LIU Xiang, ZHANG Xing. Adaptive Enhancement Algorithm for Non-uniform Illumination Images [J]. Computer Engineering and Applications, 2021, 57(21): 216-223.
[2]	TIAN Qichuan, MENG Ying. Image Semantic Segmentation Algorithm with Multi-scale Feature Fusion and Enhancement [J]. Computer Engineering and Applications, 2021, 57(2): 177-185.
[3]	BAO Shun, ZHOU Ligang. Multiple Attribute Decision Making Method Based on Additive Consistent Pythagorean Fuzzy Preference Relations [J]. Computer Engineering and Applications, 2021, 57(17): 61-67.
[4]	ZHANG Junfang, ZHOU Ligang, ZHANG Chunfang, XU Xin, XIAO Jian. Application of Interval-Valued Pythagorean Hesitant Fuzzy Continuous Entropy-Grey Relative Degree [J]. Computer Engineering and Applications, 2021, 57(15): 207-214.
[5]	WANG Xiaopeng, YANG Wenting, WEN Haotian. Remote Sensing Water Image Enhancement Using Adaptive Morphology [J]. Computer Engineering and Applications, 2021, 57(12): 186-192.
[6]	DU Yuyi, WANG Pengzhang, DUO Huaqiong, DONG Yingda. Enhancement Algorithm of Mongolian Furniture Pattern Based on Quantile in Frequency Domain [J]. Computer Engineering and Applications, 2021, 57(12): 207-210.
[7]	HAN Jipu, DUAN Xianhua, CHANG Zhen. Target Segmentation Algorithm Based on SLIC and Region Growing [J]. Computer Engineering and Applications, 2021, 57(1): 213-218.
[8]	CHEN Yingchun. Fast Low-Light Image Enhancement Based on Tone Mapping [J]. Computer Engineering and Applications, 2020, 56(9): 234-239.
[9]	SUN Xinling, ZHANG Hao, ZHAO Li. Video Object Tracking Based on Scale Estimation MST and Particle Filtering [J]. Computer Engineering and Applications, 2020, 56(8): 117-123.
[10]	JIANG Jiao, CAI Linqin, WEI Pengcheng, LI Li. Aretrieval Scheme Supporting Verifiable Ciphertext Fuzzy Keyword [J]. Computer Engineering and Applications, 2020, 56(7): 74-80.
[11]	SONG Juan. Pythagorean Decision-Making Model Considering Fusion Algorithms and Cross-Entropy [J]. Computer Engineering and Applications, 2020, 56(5): 74-79.
[12]	YUAN Xiaoping, CUI Qiwen, CHENG Gan, ZHANG Xia, ZHANG Yi, WANG Suyuan. Improved Gabor Self Quotient Image Algorithm and Its Application to Face Recognition [J]. Computer Engineering and Applications, 2020, 56(5): 206-213.
[13]	WANG Yonggui, MEI Xuanwei. Fuzzy Recommendation Algorithm for Asymmetric Heterogeneous Information Network [J]. Computer Engineering and Applications, 2020, 56(23): 74-79.
[14]	ZHANG Jianhua, LI Fangfang, YANG Lan. Research on Matching Supply and Demand of Case Knowledge Based on PFS and RS [J]. Computer Engineering and Applications, 2020, 56(23): 139-145.
[15]	SU Mengke, KONG Xiangzhi. Application of Intuitionistic Dual Hesitant Fuzzy Sets in Multi-attribute Group Decision Making [J]. Computer Engineering and Applications, 2020, 56(17): 273-278.

Infrared object segmentation based on fuzzy enhancement and mean shift

应用模糊增强及均值漂移实现红外目标分割

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics