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夏平 , 任强 , 吴涛 , 等 . 融合多尺度统计信息模糊C均值聚类与Markov随机场的小波域声纳图像分割 [J ] . 兵工学报 , 2017 , 38 ( 5 ): 940 - 948 . DOI: 10.3969/j.issn.1000-1093.2017.05.014 http://doi.org/10.3969/j.issn.1000-1093.2017.05.014 声纳图像成像质量差、特征信息弱，目标分割存在一定困难，为此提出一种融合多尺度统计信息的模糊C均值(FCM)聚类与Markov随机场(MRF)的小波域声纳图像分割算法。小波域中低频信息统计特性描述了低频不同区域像素聚类情况，高频信息反映了该方向纹理特征，依据低频子带的统计峰值选取FCM初始聚类中心，应用小波域FCM聚类算法对声纳图像进行预分割，抑制噪声的影响，提高了预分割的准确性；构建初分割后图像的多尺度MRF模型，尺度间节点标记的相关性采用1阶Markov性表征，尺度内构建2阶邻域系统描述系数间的标记联系，标记场采用双点多级逻辑模型建模，同一标记的系数特征场采用高斯模型建模，弥补了MRF算法中层次信息和轮廓信息描述的不足；应用迭代条件模型算法求其最小能量下的标记场，实现声纳图像分割。从视觉主观效果和客观评价指标两方面的实验结果验证表明，该算法分割声纳图像均优于FCM聚类算法和MRF算法，分割的声纳图像边缘与细节的清晰度、精细度均有一定程度改善。

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