摘要
对水下声纳图像进行目标分割是非常复杂的,它不仅取决于被分割的不同目标,还与海底混响噪声、背景区域有着紧密的联系。通过分析声纳图像的特点,提出了一种新的声纳图像自动分割方法,即利用一种快速的模糊C均值聚类方法来完成初始分割,然后利用初始分割结果对马尔可夫模型的初始参数进行估计,最后,根据马尔可夫理论进行迭代条件估计,得到精确的图像分割结果。最后利用实测数据,验证了此种算法的可行性和有效性。
Segmentation of underwater objects using sonar imagery is complicated by the variability of objects,noises,and background.Through analyzing the features of sonar imagery,a kind of new automatic segmentation algorithm for sonar imagery is proposed.A fast fuzzy C-mean clustering algorithm is adopted to complete initial segmentation.Then according to the initial segmentation results,the initial parameters of the Markov random field models are estimated.Lastly,iterative conditional estimation based on the Markov random field theory is used to obtain the final precise segmentation resuhs.In the last part,experiments are conducted to demonstrate the feasibility and effectiveness by the data detected practically.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第2期168-172,共5页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)No.2006AA04Z248~~
关键词
马尔可夫随机场
声纳图像
分割
Markov random field
sonar imagery
segmentation
