基于改进模糊C均值的海面目标图像分割算法
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摘要
提出一种图像分割算法,解决水面无人艇在执行目标跟踪与识别任务过程中的图像快速准备分割问题。首先使用均值滤波算法对彩色的海洋背景图像进行滤波,同时利用其非参数性得到图像的聚类中心和类别数,并以此作为初始化参数进行图像的模糊C均值聚类,在此基础上进行大津法Otsu二值化处理实现目标提取。使用BSDS500标准数据集和海洋背景图像对算法的分割效果及效率进行验证,与传统的模糊C均值算法、脉冲耦合神经网络算法、自适应遗传算法以及马尔科夫随机场算法进行对比的结果显示了该算法的有效性。
We propose an image segmentation algorithm to solve the problem of fast image segmentation when surface unmanned vehicles perform target tracking and recognition tasks. Firstly, the algorithm uses the mean filtering algorithm to filter the RGB ocean background image, and uses its nonparametric property to obtain the clustering center of the image and the number of categories, which are used as the initialization parameters to perform fuzzy C-means clustering on the image. On this basis, the image is binarized by the Otsu method to realize target extraction. The BSDS500 standard dataset and marine background images are used to verify itse segmentation effect and efficiency. Comparison with the traditional fuzzy C-means algorithm, pulse coupled neural network algorithm, adaptive genetic algorithm and Markov random field algorithm, proves the effectiveness of the proposed algorithm.
We propose an image segmentation algorithm to solve the problem of fast image segmentation when surface unmanned vehicles perform target tracking and recognition tasks. Firstly, the algorithm uses the mean filtering algorithm to filter the RGB ocean background image, and uses its nonparametric property to obtain the clustering center of the image and the number of categories, which are used as the initialization parameters to perform fuzzy C-means clustering on the image. On this basis, the image is binarized by the Otsu method to realize target extraction. The BSDS500 standard dataset and marine background images are used to verify itse segmentation effect and efficiency. Comparison with the traditional fuzzy C-means algorithm, pulse coupled neural network algorithm, adaptive genetic algorithm and Markov random field algorithm, proves the effectiveness of the proposed algorithm.
引文
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[4] Lai C S,Jia Y,Mcculloch M,et al.Daily clearness index profiles cluster analysis for photovoltaic system[J].IEEE Transactions on Industrial Informatics,2017,13(5):2322-2332.
[5] Chattaraj A,Arpita D.Mammographie image segmentation using kernel based FCM clustering approach[C]//Proc of 2016 International Conference on Computer,Electrical & Communication Engineering (ICCECE),2016:1-6.
[6] Zhang H,Wang Q,Shi W,et al.A novel adaptive fuzzy local information C-Means clustering algorithm for remotely sensed imagery classification[J].IEEE Geoscience and Remote Sensing Society,2017,55(9):5057-5068.
[7] Li F,Qin J.Robust fuzzy local information and Lp-norm distance-based image segmentation method[J].IET Image Processing,2017,11(4):217-226.
[8] Bai X,Chen Z,Zhang Y,et al.Infrared ship target segmentation based on spatial Information improved FCM[J].IEEE Transactions on Cybernetics,2016,46(12):3259-3271.
[9] Ngatchou A,Bitjoka L,Mfoumou E,et al.Robust and fast segmentation based on fuzzy clustering combined with unsupervised histogram analysis[J].IEEE Intelligent Systems,2017,32(5):6-13.
[10] Zhou Fang-fang,Fan Xiao-ping,Ye Zhen.Mean shift research and applications[J].Control and Decision,2007,22(8):841-847.(in Chinese)
[11] Guo Jia,Liu Xiao-yu,Wu Bing,et al.Binarisation method for images acquired under non-uniform illumination[J].Computer Applications and Software,2014(3):183-186.(in Chinese)
[12] Ma Yi-de,Dai Ruo-lan,Li Lian.Automated image segmentation using pulse coupled neural networks and image’s entropy[J].Journal of China Institute of Communications,2002,23(1):46-51.(in Chinese)
[13] Wang Xiao-ling,Wang Meng-jun,Zhou Ya-tong.Feature extracting of license plate character based on Eucliden-like distance transform[J].Computer Simulation,2014,31(4):184-187.(in Chinese)
[14] Wang F,Li J,Liu S,et al.An improved adaptive genetic algorithm for image segmentation and vision alignment used in microelectronic bonding[J].IEEE/ASME Transactions on Mechatronics,2014,19(3):916-923.
[15] Cao Jia-zi,Song Ai-guo.Research on the texture image segmentation method based on Markov Random Field [J].Chinese Journal of Scientific Instrument,2015,36(4):776-786.(in Chinese)
[16] Qian Xiao-hua,Guo Shu-xu,Li Xue-yan.Image understanding based global evaluation algorithm for segmentation[J].Acta Electronica Sinica,2012,40(10):1989-1995.(in Chinese)附中文参考文献:
[10] 周芳芳,樊晓平,叶榛.均值漂移算法的研究与应用[J].控制与决策,2007,22(8):841-847.
[11] 郭佳,刘晓玉,吴冰,等.一种光照不均匀图像的二值化方法[J].计算机应用与软件,2014(3):183-186.
[12] 马义德,戴若兰,李廉.一种基于脉冲耦合神经网络和图像熵的自动图像分割方法[J].通信学报,2002,23(1):46-51.
[13] 王祥玲,王蒙军,周亚同.车牌字符的类欧几里得距离特征提取与分析[J].计算机仿真,2014,31(4):184-187.
[15] 曹家梓,宋爱国.基于马尔科夫随机场的纹理图像分割方法研究[J].仪器仪表学报,2015,36(4):776-786.
[16] 钱晓华,郭树旭,李雪妍.基于图像理解视角的分割全局评价算法[J].电子学报,2012,40(10):1989-1995.