Iterative Point Matching via multi-direction geometric serialization and reliable correspondence selection
Qian, Deheng1; Chen, Tianshi2; Qiao, Hong1,3; Tang, Tang1
Source PublicationNEUROCOMPUTING
2016-07-12
Volume197Pages:171-183
SubtypeArticle
AbstractPoint matching aims at finding the optimal matching between two sets of feature points. It is widely accomplished by graph matching methods which match nodes of graphs via minimizing energy functions. However, the obtained correspondences between feature points vary in their matching qualities. In this paper, we propose an innovative matching algorithm which iteratively improves the matching found by such methods. The intuition is that we may improve a given matching by identifying "reliable" correspondences, and re-matching the rest feature points without reliable correspondences. A critical issue here is how to identify reliable correspondences, which is addressed with two novel mechanisms, Multi-direction Geometric Serialization (MGS) and Reliable Correspondence Selection (RCS). Specifically, MGS provides representations of the spatial relations among feature points. With these representations, RCS determines whether a correspondence is reliable according to a reliability metric. By recursively applying MGS and RCS, and re-matching feature points without reliable correspondences, a new (intermediate) matching can be obtained. In this manner, our algorithm starts with a matching provided by a classical method, iteratively generates a number of intermediate matchings, and chooses the best one as the final matching. Experiments demonstrate that our algorithm significantly improves the matching precisions of classical graph matching methods. (C) 2016 Published by Elsevier B.V.
KeywordPoint Matching Order Relation Projection Graph Matching Dynamic Programming
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2016.02.066
WOS KeywordREGISTRATION ; RECOGNITION ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNational Science Foundation of China(61210009 ; Strategic Priority Research Program of the CAS(XDB02080003) ; Beijing Natural Science Foundation(2141100002014002) ; 61503383)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000376694700015
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11611
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
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Comp, State Key Lab Comp Architecture, Beijing 100190, Peoples R China
3.Chinese Acad Sci, CEBSIT, Shanghai, Peoples R China
First Author AffilicationChinese 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,et al. Iterative Point Matching via multi-direction geometric serialization and reliable correspondence selection[J]. NEUROCOMPUTING,2016,197:171-183.
APA Qian, Deheng,Chen, Tianshi,Qiao, Hong,&Tang, Tang.(2016).Iterative Point Matching via multi-direction geometric serialization and reliable correspondence selection.NEUROCOMPUTING,197,171-183.
MLA Qian, Deheng,et al."Iterative Point Matching via multi-direction geometric serialization and reliable correspondence selection".NEUROCOMPUTING 197(2016):171-183.
Files in This Item: Download All
File Name/Size DocType Version Access License
1-s2.0-S092523121600(3616KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
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
File name: 1-s2.0-S0925231216003672-main.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

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