摘要
针对声呐图像的特点,提出了一种新的基于马尔可夫随机场(MRF)的非监督声呐图像自动分割方法。研究发现,声呐图像混响区基本上都服从高斯分布,然而,其直方图离散化的分布效果不利于图像的自动分割,因此,通过一种快速有效的高斯金字塔模型对声呐图像进行预处理,使得处理后的声呐图像的海底混响区直方图服从高斯分布。在此基础上提出了一个能够自动确定声呐图像分类个数的模型,并通过该模型结合一种局部能量极值化的方法对马尔科夫模型的初始化参数进行估计,从而形成一种完全自动的声呐图像分割模型。最后,利用该模型对声呐图像数据进行了分割实验,并和其他典型的分割算法进行了比较,验证了该方法的有效性及快速性。
Utilizing the characteristics of sonar images, a new unsupervised method is proposed to segment sonar im-ages automatically based on Markov random field（ MRF） .The research demonstrated that the histogram of sonar im-ages in reverberation area obeys the rule of Gaussian distribution.However, its discrete distribution effect is not beneficial to the automatic segmentation.In this paper, a fast and effective Gaussian pyramid model is used for the preprocessing of sonar image, in an attempt to make the histogram of the bottom reverberation of these images obey Gaussian distribution.On this basis, a model that may automatically determine the number of sonar images classifi-cation is proposed.By combining this model with a local energy extremum method, the initialization parameters of the MRF model were estimated to form a fully automated sonar image segmentation model.Finally, the model can be used for segmentation experiments on the data of sonar images, and it is compared with other typical segmenta-tion algorithms, verifying the efficiency and rapidity of the method.
出处
《哈尔滨工程大学学报》
EI
CAS
CSCD
北大核心
2015年第4期516-521,共6页
Journal of Harbin Engineering University
基金
黑龙江省自然科学基金资助项目(F201416)
