摘要
针对传统分水岭算法在处理声纳图像时存在严重的过分割现象,提出一种结合分割前处理和后处理两类方法优点的算法.首先利用H-min变换技术提取区域极小值和新的标记,对标记后的图像进行分水岭图像分割;然后结合改进适应度函数的粒子群全局寻优算法,从初分割的小区域中搜索出较为准确的初始聚类中心,利用这个聚类中心和改进目标函数的模糊C-均值聚类算法,再对分割后的小区域聚类,并控制迭代次数,以提高分割速度.实验结果表明:该方法能够有效消除过分割现象,提高声纳图像处理效果,有效分割率达89%,处理时间提高30%以上.
To solve lng sonar image, a the problem of overs-egmentation in traditional watershed algorithm when process watershed segmentation algorithm based on marker and fuzzy C-means clustering （FCM） was proposed. The H-rain transform was firstly used to pick up the image region minimum and new marker. Then the marked image was segmented by watershed algorithm. After that, the im- proved particle swarm optimization （PSO） was used to find the accurate original elustering centers of FCM. With the help of these centers and the improved target function, the small regions of the initial segmented image were clustered by FCM. The iterating number was controlled to increase segment speed. The experimental results show that this method can solve the problem of over-segmentation and increase the sonar image segmentation efficiency with the effective segmentation rate of 89 ~ and the process time of more than 30 %increase.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2013年第6期50-54,共5页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
高等学校博士后专项科研基金资助项目(20090641460)
关键词
图像分割
声纳图像
分水岭算法
标记
粒子群寻优
模糊C-均值聚类
image segmentation
sonar image
watershed algorithm
marker
particle swarm optimization
fuzzy C-means clustering
