液晶与显示2016，Vol.31Issue(10)：1006-1015,10.DOI:10.3788/YJYXS20163110.1006
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
摘要
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 func-tion 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 im-prove 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.关键词
显著性/MSER/图多样性/分形维数/CPD/虚警控制器/AdaboostKey words
saliency/MSER/graph manifold ranking/fractal dim/CPD/false alarm controller/Ada-boost分类
信息技术与安全科学引用本文复制引用
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):1006-1015,10.基金项目
国家自然科学基金(No．61303192) Supported by National Natural Science Foundation of China(No．61303192) （No．61303192）
