title: BgCut: automatic ship detection from UAV images.
abstract: 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.
authors: School of Computer Software, Tianjin University, Tianjin 300072, China.
authors: Chao
authors: 0000-0002-6398-0398
authors: C
authors: Xu
authors: School of Computer Software, Tianjin University, Tianjin 300072, China.
authors: Dongping
authors: 0000-0002-1393-3948
authors: D
authors: Zhang
authors: Space Star Technology Co., Ltd., Beijing 100086, China.
authors: Zhengning
authors: Z
authors: Zhang
authors: School of Computer Software, Tianjin University, Tianjin 300072, China.
authors: Zhiyong
authors: Z
authors: Feng
mesh_terms: D000401:Aircraft; D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010781:Photography / Q000379:methods*; D011825:Radar; D012371:Robotics / Q000379:methods; D012767:Ships