Geodesic-like features for point matching
Qian, Deheng1; Chen, Tianshi2,3; Qiao, Hong3,4
Source PublicationNEUROCOMPUTING
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
Indexed BySCI
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
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Qian, Deheng]'s Articles
[Chen, Tianshi]'s Articles
[Qiao, Hong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Qian, Deheng]'s Articles
[Chen, Tianshi]'s Articles
[Qiao, Hong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Qian, Deheng]'s Articles
[Chen, Tianshi]'s Articles
[Qiao, Hong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.