Introducing locally affine-invariance constraints into lunar surface image correspondence

doi:10.1016/j.neucom.2015.12.082

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Introducing locally affine-invariance constraints into lunar surface image correspondence
	Zhang, Yu-Ren1 ; Yang, Xu1 ; Qiao, Hong1,3 ; Liu, Zhi-Yong1 ; Liu, Chuan-Kai 2
发表期刊	NEUROCOMPUTING
	2016-04-19
卷号	186 期号:无页码:258-270
文章类型	Article
摘要	This paper aims to solve the keypoint correspondence problem in lunar surface images, a typical correspondence task under point ambiguity. Point ambiguity may be caused by repetitive patterns, cluttered scenes, and outliers in the images, which makes the local descriptors less discriminative. In this paper we introduce locally affine-invariance constraints on graphs to tackle the keypoint correspondence problem under point ambiguity. The key idea is that each point can be represented with the affine combination of its neighbors. It is suitable for our problem because it is not only invariant to scale and rotational change, but also more resistant to outliers. Specifically, we introduce the locally affine-invariance constraints into the subgraph matching problem and the common subgraph matching problem. The locally affine-invariance constraint is not directly applicable on common subgraph matching due to its dependency on awareness of selected keypoints. This problem is approximately addressed by solving a series of reliable matching identification and rematching problems. In the experiments, we first apply the proposed method on standard graph matching datasets to evaluate its effectiveness on general correspondence problem under point ambiguity, and second validate the applicability on the lunar surface image dataset. (C) 2016 Elsevier B.V. All rights reserved.
关键词	Keypoint Correspondence Point Ambiguity Graph Matching Lunar Image Processing
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2015.12.082
关键词[WOS]	ROBUST ; GNCCP
收录类别	SCI
语种	英语
项目资助者	National Science Foundation of China (NSFC)(61375005 ; Beijing Municipal Science & Technology Commission(2141100002014002) ; National Key Technology RD Program(2012BAI34B02) ; 61033011 ; 61210009 ; 61101221)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000374366300025
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11629
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Yang, Xu
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Beijing Aerosp Flight Control Ctr, Beijing, Peoples R China 3.Chinese Acad Sci, CEBSIT, Shanghai, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Zhang, Yu-Ren,Yang, Xu,Qiao, Hong,et al. Introducing locally affine-invariance constraints into lunar surface image correspondence[J]. NEUROCOMPUTING,2016,186(无):258-270.
APA	Zhang, Yu-Ren,Yang, Xu,Qiao, Hong,Liu, Zhi-Yong,&Liu, Chuan-Kai.(2016).Introducing locally affine-invariance constraints into lunar surface image correspondence.NEUROCOMPUTING,186(无),258-270.
MLA	Zhang, Yu-Ren,et al."Introducing locally affine-invariance constraints into lunar surface image correspondence".NEUROCOMPUTING 186.无(2016):258-270.

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