BgCut: Automatic Ship Detection from UAV Images
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 incl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/171978 |
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| _version_ | 1832557076283392000 |
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| author | Chao Xu Dongping Zhang Zhengning Zhang Zhiyong Feng |
| author_facet | Chao Xu Dongping Zhang Zhengning Zhang Zhiyong Feng |
| author_sort | Chao Xu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1c9a8cb93ad14e0a99f42f838b90912e |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-1c9a8cb93ad14e0a99f42f838b90912e2025-02-03T05:43:40ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/171978171978BgCut: Automatic Ship Detection from UAV ImagesChao Xu0Dongping Zhang1Zhengning Zhang2Zhiyong Feng3School of Computer Software, Tianjin University, Tianjin 300072, ChinaSchool of Computer Software, Tianjin University, Tianjin 300072, ChinaSpace Star Technology Co., Ltd., Beijing 100086, ChinaSchool of Computer Software, Tianjin University, Tianjin 300072, ChinaShip 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.http://dx.doi.org/10.1155/2014/171978 |
| spellingShingle | Chao Xu Dongping Zhang Zhengning Zhang Zhiyong Feng BgCut: Automatic Ship Detection from UAV Images The Scientific World Journal |
| title | BgCut: Automatic Ship Detection from UAV Images |
| title_full | BgCut: Automatic Ship Detection from UAV Images |
| title_fullStr | BgCut: Automatic Ship Detection from UAV Images |
| title_full_unstemmed | BgCut: Automatic Ship Detection from UAV Images |
| title_short | BgCut: Automatic Ship Detection from UAV Images |
| title_sort | bgcut automatic ship detection from uav images |
| url | http://dx.doi.org/10.1155/2014/171978 |
| work_keys_str_mv | AT chaoxu bgcutautomaticshipdetectionfromuavimages AT dongpingzhang bgcutautomaticshipdetectionfromuavimages AT zhengningzhang bgcutautomaticshipdetectionfromuavimages AT zhiyongfeng bgcutautomaticshipdetectionfromuavimages |