应用图像融合与多样性的舰船显著性检测

郭少军; 娄树理; 刘峰

doi:10.3788/YJYXS20163110.1006

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应用图像融合与多样性的舰船显著性检测

显示与图像 | 更新时间：2020-07-07

- 应用图像融合与多样性的舰船显著性检测
- Ship-target saliency detection via image fusion and graph-based manifold ranking
- 液晶与显示 2016年31卷第10期页码：1006-1015
- 作者机构：
  
  海军航空工程学院控制科学与工程系, 山东烟台 264001
- 作者简介：
  
  郭少军(1985-), 男, 湖南洞口人, 博士研究生, 助理工程师, 2008年于海军航空工程学院获得学士学位, 2011年于海军航空工程学院获得硕士学位, 主要从事目标检测与识别, 计算机视觉等方面的研究。E-mail:guoba2000@163.com
  [ "娄树理(1976-), 男, 山东蒙阴人, 博士, 副教授, 2004年于海军航空工程学院获得硕士学位, 2011年与海军航空工程学院获得博士学位, 现为海军航空工程学院控制工程系副教授, 主要从事多源目标成像技术, 目标检测与识别等研究。E-mail:shulilou@sina.com" ]
  [ "刘峰(1988-), 男, 黑龙江哈尔滨人, 博士研究生, 2011年于华中科技大学获得学士学位, 2013年于海军航空工程学院获得硕士学位, 主要从事计算机视觉, 图像处理与目标识别等方面的研究。E-mail:liufeng_cv@126.com" ]
- 基金信息：
  
  国家自然科学基金(61303192);National Natural Science Foundation of China(61303192)
- DOI：10.3788/YJYXS20163110.1006
  中图分类号： TP391
- 收稿日期：2016-06-01，
  
  录用日期：2016-7-22，
  
  纸质出版日期：2016-10
- 稿件说明：
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郭少军, 娄树理, 刘峰. 应用图像融合与多样性的舰船显著性检测[J]. 液晶与显示, 2016,31(10):1006-1015.

Shao-jun GUO, Shu-li LOU, Feng LIU. Ship-target saliency detection via image fusion and graph-based manifold ranking[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 1006-1015.
郭少军, 娄树理, 刘峰. 应用图像融合与多样性的舰船显著性检测[J]. 液晶与显示, 2016,31(10):1006-1015. DOI： 10.3788/YJYXS20163110.1006.

Shao-jun GUO, Shu-li LOU, Feng LIU. Ship-target saliency detection via image fusion and graph-based manifold ranking[J]. Chinese journal of liquid crystals and displays, 2016, 31(10): 1006-1015. DOI： 10.3788/YJYXS20163110.1006.

摘要

基于单源的图像显著性检测存在较大的虚警或漏检，文章提出了利用约简后的特征点和CPD算法对海面实拍船只图像进行多源图像匹配，获得图像间的变换投影方程并利用投影方程对单源图像的显著性检测结果进行叠加与虚警控制器分类，从而达到提高检测率与控制虚警的目的。显著性检测方面，文章分析了基于图等级多样性的显著性检测方法的不足和优点，引入了最大稳定区域检测方法对图像做前期处理，并对获得区域进行联合获得新区域，使得新区域能够最大限度地满足基于图的等级多样性显著性检测最优条件。对于检测获得的联合区域目标显著性不完整的情况，利用了区域的叠加性进行加权求和，最终获得了具有较好联通性的多舰船目标图像显著性检测结果。对于显著性检测结果中存在较大虚警的情况，文章进一步提出计算船只与浪花的多尺度分形维数，并结合Adaboost算法训练浪花虚警控制器。实验结果显示控制器能够消除一部分浪花带来的虚警，但是对于灰度与舰船极为相似的虚警无法消除。

Abstract

Objects saliency detected by single-source image always include a lot of false alarm and leak detection. This work proposes to simplify the feature points data first and then uses it to match the multi-source images

after which we can get a mapping function between the couple images. The function is used to match the saliency results between the images so as to improve the object detection rate and reduce the leak detection rate. For the saliency detection of the objects

this work propose to improve the saliency detection via graph-based manifold ranking method by MSER. We firstly detect the MSER regions and union the regions

which can make every region suit the request of that method. And after the saliency detection of every union regions we sum the saliency maps by weight W. The summing process unions the saliency objects well and can reduce the false alarm. But false alarm still exist. This work propose to compute a false alarm controller to lower the false alarm by associating the multi-fractal dim and Adaboost method

which works well for wave false alarm reduction but not so good for false alarms like the ships very much.

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references

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