BgCut: Automatic Ship Detection from UAV Images

被引：9

作者：

Xu, Chao ^{[1
]}

Zhang, Dongping ^{[1
]}

Zhang, Zhengning ^{[2
]}

Feng, Zhiyong ^{[1
]}

机构：

[1] Tianjin Univ, Sch Comp Software, Tianjin 300072, Peoples R China

[2] Space Star Technol Co Ltd, Beijing 100086, Peoples R China

来源：

SCIENTIFIC WORLD JOURNAL | 2014年

基金：

中国国家自然科学基金;

关键词：

SEGMENTATION; ALGORITHM; TEXTURE;

D O I：

10.1155/2014/171978

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches.

引用

页数：11

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