Pubblicazione scientifica

BgCut: Automatic Ship Detection from UAV Images

FILONE TECNOLOGICO	TEMA
economia circolare e sostenibilità

RIASSUNTO

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.

DATA

Data di pubblicazione: 01/01/2014

AUTORI

XU CHAO
ZHANG DONGPING
ZHANG ZHENGNING
FENG ZHIYONG

ENTE DI AFFERENZA

SCHOOL OF COMPUTER SOFTWARE TIANJIN UNIV TIANJIN 300072 CHINA
SPACE STAR TECH CO LTD

RIVISTA

The Scientific World Journal, 2014, 171978

IMPACT FACTOR

1,460

indietro