Geodesic-like features for point matching
Qian, Deheng1; Chen, Tianshi2,3; Qiao, Hong3,4
Source PublicationNEUROCOMPUTING
2016-12-19
Volume218Issue:Pages:401-410
SubtypeArticle
AbstractPoint 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.
KeywordPoint Matching Non-rigid Deformation Geodesic Distance
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2016.08.092
WOS KeywordDIMENSIONALITY REDUCTION ; IMAGE REGISTRATION ; RECOGNITION ; SURFACES
Indexed BySCI
Language英语
Funding OrganizationStrategic Priority Research Program, Chinese Academy of Sciences(XDB02080003) ; BMST(D16110400140000 ; National Natrual Science Fundation of China(61522211 ; D161100001416001) ; 61473275)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000388053700044
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12613
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorQiao, Hong
Affiliation1.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
Recommended Citation
GB/T 7714
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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