基于改进K-均值算法在彩色图像分割中的应用  

Color image segmentation method based on improved K-means clustering algorithm

作　　者：刘盈盈[1] 石跃祥[1] 莫浩澜[1] 

机构地区：[1]湘潭大学信息工程学院,湖南湘潭411105

出　　处：《计算机工程与应用》2008年第29期191-192,203,共3页Computer Engineering and Applications

基　　金：博士科研启动项目(No.06QDZ23);交叉项目(No.06IND12)

摘　　要：如何对彩色图像中的目标进行有效的分割是计算机视觉和图像分析的重点和难点,文中提出不断对彩色图像采用最优阈值化进行一次粗分割提取最大目标区域,再利用改进的K-均值算法对提取目标子区域进行精确分割。实验结果表明该方法对彩色图像能够有效地提取目标物体,并对噪声图像具有一定的鲁棒性。How to effectively segment objects in the color images is the key point in the computer vision and image analysis. This paper presents repeated using the optimal threshold for a roughly extract the largest target area of the color image.Then improved K_means clustering algorithm is used to improve the accuracy of the segmentation from the target area.Experimental results show that this method can effectively extract color image of the object.It is also a certain degree of robustness to the noise image.

关 键 词：图像分割 最优阈值化 K-均值算法 鲁棒性 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]