林洋,
1.西安邮电大学通信与信息工程学院 ??西安 ??710121
2.电子信息现场勘验应用技术公安部重点实验室 ??西安 ??710121
3.陕西省无线通信与信息处理技术国际合作研究中心 ??西安 ??710121
基金项目:国家自然科学基金(61571361, 61671377)，陕西省教育厅科学研究计划(16JK1709)，西安邮电大学西邮新星团队计划(xyt2016-01)
详细信息
作者简介:兰蓉：女，1977年生，博士，副教授，研究方向为模式识别和图像处理
林洋：男，1993年生，硕士生，研究方向为图像处理
通讯作者:林洋 784046805@qq.com
中图分类号:TP391计量
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被引次数:0
出版历程
收稿日期:2018-07-03
修回日期:2018-12-29
网络出版日期:2019-01-07
刊出日期:2019-06-01
Suppressed Non-local Spatial Intuitionistic Fuzzy C-means Image Segmentation Algorithm
Rong LAN,Yang LIN,
1. School of Telecommunications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
2. Key Laboratory of Electronic Information Application Technology for Scene Investigation, Ministry of Public Security, Xi’an 710121, China
3. International Joint Research Center for Wireless Communication and Information Processing, Shannxi Province Xi’an 710121, China
Funds:The National Natural Science Foundation of China (61571361, 61671377), Shaanxi Provincial Department of Education Scientific Research Plan (16JK1709), New Star Team of Xi’an University of Posts and Telecommunications (xyt2016-01)
摘要
摘要:针对传统的模糊C-均值(FCM)算法没有考虑图像像素的空间邻域信息，对噪声敏感，算法收敛较慢等问题，该文提出一种抑制式非局部空间直觉模糊C-均值图像分割算法。首先，通过计算像素的非局部空间信息提高抗噪能力，克服传统的FCM算法只考虑图像单个像素的灰度特征信息的缺陷，提高分割精度。其次，根据直觉模糊集理论，通过“投票模型”自适应生成犹豫度作为抑制因子修正隶属度，提高算法的运行效率。实验结果表明，该算法对噪声鲁棒性较强并且有较好的分割性能。
关键词:图像分割/
模糊C-均值/
直觉模糊集/
非局部空间信息/
犹豫度
Abstract:In order to deal with these issues of the traditional Fuzzy C-Means (FCM) algorithm, such as without consideration of the spatial neighborhood information of pixels, noise sensitivity and low convergence speed, a suppressed non-local spatial intuitionistic fuzzy c-means image segmentation algorithm is proposed. Firstly, in order to improve the accuracy of segmentation image, the non-local spatial information of pixel is used to improve anti-noise ability, and to overcome the shortcomings of the traditional FCM algorithm, which only considers the gray characteristic information of single pixel. Secondly, by using the ‘voting model’ based on the intuitionistic fuzzy set theory, the hesitation degrees are adaptively generated as inhibitory factors to modify the membership degrees, and then the operating efficiency is increased. Experimental results show that the new algorithm is robust to noise and has better segmentation performance.
Key words:Image segmentation/
Fuzzy C-Means (FCM)/
Intuitionistic fuzzy set/
Non-local spatial information/
Hesitation degree
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