基于超像素聚类的侧扫声呐图像分割算法

doi:10.19678/j.issn.1000-3428.0048300

计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 219-225,232. doi: 10.19678/j.issn.1000-3428.0048300

基于超像素聚类的侧扫声呐图像分割算法

盛蕴霞 ¹,霍冠英 ^1,2,刘静 ¹

1.河海大学物联网工程学院,江苏常州213022;2.常州市传感网与环境感知重点实验室,江苏常州 213022

收稿日期:2017-08-09 出版日期:2018-06-15 发布日期:2018-06-15
作者简介:盛蕴霞(1992—),女,硕士研究生,主研方向为声呐图像分割、水下三维重建;霍冠英(通信作者),副教授、博士;刘静,硕士研究生。
基金资助:
国家自然科学基金(41306089);江苏省自然科学基金(BK20130240);中央高校基本科研业务费专项资金(2017B43114)。

Side-scan Sonar Image Segmentation Algorithm Based on Super-pixels Clustering

SHENG Yunxia¹,HUO Guanying^1,2,LIU Jing ¹

1.College of Internet of Things Engineering,Hohai University,Changzhou,Jiangsu 213022,China;2.Changzhou Key Laboratory of Sensor Networks and Environmental Perception,Changzhou,Jiangsu 213022,China

Received:2017-08-09 Online:2018-06-15 Published:2018-06-15

摘要/Abstract

摘要：

针对传统超像素分割对声呐图像存在抗噪性能较差、分割后区域难以准确合并等问题,提出一种以超像素聚类方式实现侧扫声呐图像分割的方法。采用快速双边滤波对待分割的侧扫声呐图像进行降噪处理,以降低后续分割的困难。对降噪后的侧扫声呐图像提取亮度特征和纹理特征,计算两者相似性,并进行加权融合,以融合后的相似性作为像素与聚类中心间的距离度量准则,从而生成超像素。基于亮度特征对超像素进行显著性检测,标记显著性超像素,并基于最大流-最小割方法对超像素进行聚类。计算类内显著性超像素占比,将其与预设阈值进行比较,将大于阈值的标记为前景类,反之则为背景类,以得到最终的分割结果。实验结果表明,与模糊局部信息C均值算法和简单线性迭代聚类算法相比,该算法的分割准确率较高、过分割和欠分割率较低。

关键词: 超像素分割, 聚类, 侧扫声呐图像, 最大流-最小割, 显著性

Abstract:

Traditional super-pixels segmentation method has problems of poor anti-noise performance,inaccurate merging and other issues.To solve these problems,a method of segmentation of side-scan sonar images by super-pixel clustering is proposed.The fast bilateral filter is used to perform the noise reduction processing to reduce the difficulty of subsequent segmentation.The luminance and texture features of the de-noised side-scan sonar images are extracted,and the similarities of the two features are calculated.These similarities are combined with weights to give the distance metric between the pixels and the cluster centers to generate the super-pixels.The saliency super-pixels are labeled based on the luminance feature,and all super-pixels are clustered by the maximum flow and minimum cut method.The proportions of the super-pixels with saliency within the clusters are calculated,and they are compared with the preset threshold.Clusters with proportion which is larger than the threshold are marked as foreground;otherwise,they are marked as background.As a result,the final segmentation is obtained.Experimental results show that compared with Fuzzy Local Information C-Means (FLICM) algorithm and Simple Linear Iterative Clustering (SLIC) algorithm,the segmentation accuracy of the proposed algorithm is high,and over-division rate and under-division rate are low.

Key words: super-pixel segmentation, clustering, side-scan sonar image, maximum flow-minimum cut, saliency

中图分类号:

TP391.4

盛蕴霞,霍冠英,刘静. 基于超像素聚类的侧扫声呐图像分割算法[J]. 计算机工程, 2018, 44(6): 219-225,232.

SHENG Yunxia,HUO Guanying,LIU Jing. Side-scan Sonar Image Segmentation Algorithm Based on Super-pixels Clustering[J]. Computer Engineering, 2018, 44(6): 219-225,232.

https://www.ecice06.com/CN/Y2018/V44/I6/219

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