Geodesic-like features for point matching
文献类型：期刊论文
| 作者 | Qian, Deheng1 ; Chen, Tianshi2,3; Qiao, Hong3,4
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2016-12-19 |
| 卷号 | 218期号:无页码:401-410 |
| 关键词 | Point matching Non-rigid deformation Geodesic distance |
| 通讯作者 | Qiao, Hong |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | DIMENSIONALITY REDUCTION ; IMAGE REGISTRATION ; RECOGNITION ; SURFACES |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000388053700044 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12613]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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