摘要
水下环境复杂多变,导致声呐技术成像后的图像质量差,影响目标识别。为此,提出一种基于Contourlet域下多尺度高斯马尔可夫随机场(GMRF)模型的水平集声呐图像分割算法。采用Contourlet变换及逆变换获取声呐图像各尺度层下的纹理特征,通过GMRF对各层纹理特征建模,以描述局部结构空间信息并降低对噪声的敏感度。根据各层纹理特征模型,对声呐图像进行由粗到细尺度的水平集分割以得到分割结果。实验结果表明,该算法在不同声呐图像中的分割准确度超过90 %,优于Otsu算法,且具有较低的复杂度和较强的鲁棒性。
Complex and changeable underwater environment leads to the poor quality of sonar images,decreasing the accuracy of target recognition.Therefore,a level set sonar image segmentation algorithm based on multiscale Gaussian Markov Random Field(GMRF) model under Contourlet domain is proposed.Contourlet transform and inverse transform are used to obtain the texture feature under each scale layer of the sonar image.The texture feature of each layer is modeled by GMRF to describe the local structure spatial information and reduce the sensitivity to noise.Based on the texture feature models of each layer,coarse-to-fine segmentation for level sets is performed on sonar images to obtain segmentation results.Experimental results show that the accuracy of the algorithm exceeds 90 % in different sonar images,which is better than Otsu algorithm and has lower complexity and stronger robustness.
作者
李鹏
陈嘉琦
马味敏
叶方跃
LI Peng;CHEN Jiaqi;MA Weimin;YE Fangyue(Jiangsu Key Laboratory of Meteorological Observation and Information Processing,Nanjing University of Information Science and Technology,Nanjing 210044,China;Jiangsu Technology and Engineering Center of Meteorological Sensor Network,Nanjing University of Information Science and Technology,Nanjing 210044,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2019年第9期253-259,共7页
Computer Engineering
基金
国家自然科学基金(41075115)
江苏省重点研发计划社会发展项目(BE201569)
江苏省“六大人才高峰”第十一批高层次人才项目(2014-XXRJ-006)
