Geodesic-like features for point matching
Qian, Deheng1; Chen, Tianshi2,3; Qiao, Hong3,4
2016-12-19
发表期刊NEUROCOMPUTING
卷号218期号:页码:401-410
文章类型Article
摘要Point matching problem seeks the optimal correspondences between two sets of points via minimizing the dissimilarities of the corresponded features. The features are widely represented by a graph model consisting of nodes and edges, where each node represents one key point and each edge describes the pair-wise relations between its end nodes. The edges are typically measured depending on the Euclidian distances between their end nodes, which is, however, not suitable for objects with non-rigid deformations. In this paper, we notice that all the key points are spanning on a manifold which is the surface of the target object. The distance measurement on a manifold, geodesic distance, is robust under non-rigid deformations. Hence, we first estimate the manifold depending on the key points and concisely represent the estimation by a graph model called the Geodesic Graph Model (GGM). Then, we calculate the distance measurement on GGM, which is called the geodesic-like distance, to approximate the geodesic distance. The geodesic-like distance can better tackle non-rigid deformations. To further improve the robustness of the geodesic-like distance, a weight setting process and a discretization process are proposed. The discretization process produces the geodesic-like features for the point matching problem. We conduct multiple experiments over widely used datasets and demonstrate the effectiveness of our method. (C) 2016 Elsevier B.V. All rights reserved.
关键词Point Matching Non-rigid Deformation Geodesic Distance
WOS标题词Science & Technology ; Technology
DOI10.1016/j.neucom.2016.08.092
关键词[WOS]DIMENSIONALITY REDUCTION ; IMAGE REGISTRATION ; RECOGNITION ; SURFACES
收录类别SCI
语种英语
项目资助者Strategic Priority Research Program, Chinese Academy of Sciences(XDB02080003) ; BMST(D16110400140000 ; National Natrual Science Fundation of China(61522211 ; D161100001416001) ; 61473275)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000388053700044
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12613
专题复杂系统管理与控制国家重点实验室_机器人理论与应用
通讯作者Qiao, Hong
作者单位1.Samsung Res Inst China Beijing SRC B, Beijing 100028, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
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Qian, Deheng,Chen, Tianshi,Qiao, Hong. Geodesic-like features for point matching[J]. NEUROCOMPUTING,2016,218(无):401-410.
APA Qian, Deheng,Chen, Tianshi,&Qiao, Hong.(2016).Geodesic-like features for point matching.NEUROCOMPUTING,218(无),401-410.
MLA Qian, Deheng,et al."Geodesic-like features for point matching".NEUROCOMPUTING 218.无(2016):401-410.
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