Improving Level Set Method for Fast Auroral Oval Segmentation

doi:10.1109/TIP.2014.2321506

OPT OpenIR > 光谱成像技术研究室

	Improving Level Set Method for Fast Auroral Oval Segmentation
	Yang, Xi 1; Gao, Xinbo 1; Tao, Dacheng2,3 ; Li, Xuelong4
作者部门	光学影像学习与分析中心
	2014-07-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1070-986X
卷号	23 期号:7 页码:2854-2865
摘要	Auroral oval segmentation from ultraviolet imager images is of significance in the field of spatial physics. Compared with various existing image segmentation methods, level set is a promising auroral oval segmentation method with satisfactory precision. However, the traditional level set methods are time consuming, which is not suitable for the processing of large aurora image database. For this purpose, an improving level set method is proposed for fast auroral oval segmentation. The proposed algorithm combines four strategies to solve the four problems leading to the high-time complexity. The first two strategies, including our shape knowledge-based initial evolving curve and neighbor embedded level set formulation, can not only accelerate the segmentation process but also improve the segmentation accuracy. And then, the latter two strategies, including the universal lattice Boltzmann method and sparse field method, can further reduce the time cost with an unlimited time step and narrow band computation. Experimental results illustrate that the proposed algorithm achieves satisfactory performance for auroral oval segmentation within a very short processing time.
文章类型	Article
关键词	Auroral Oval Segmentation Shape Knowledge Reinitialization Lattice Boltzmann Method Sparse Field Method
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TIP.2014.2321506
收录类别	SCI ; EI
关键词[WOS]	GEODESIC ACTIVE CONTOURS ; LATTICE BOLTZMANN METHOD ; IMAGE SEGMENTATION ; CURVE EVOLUTION ; MODELS ; PROPAGATION ; FORMULATION ; FLOWS ; EDGES ; GPU
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000337141400008
引用统计	被引频次：65[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/22376
专题	光谱成像技术研究室
作者单位	1.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Ultimo, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Ultimo, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Yang, Xi,Gao, Xinbo,Tao, Dacheng,et al. Improving Level Set Method for Fast Auroral Oval Segmentation[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2014,23(7):2854-2865.
APA	Yang, Xi,Gao, Xinbo,Tao, Dacheng,&Li, Xuelong.(2014).Improving Level Set Method for Fast Auroral Oval Segmentation.IEEE TRANSACTIONS ON IMAGE PROCESSING,23(7),2854-2865.
MLA	Yang, Xi,et al."Improving Level Set Method for Fast Auroral Oval Segmentation".IEEE TRANSACTIONS ON IMAGE PROCESSING 23.7(2014):2854-2865.

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