Geodesic-like features for point matching | |
Qian, Deheng1; Chen, Tianshi2,3; Qiao, Hong3,4 | |
发表期刊 | NEUROCOMPUTING |
2016-12-19 | |
卷号 | 218期号:无页码: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 |
DOI | 10.1016/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 |
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 | 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. |
条目包含的文件 | 条目无相关文件。 |
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