Geodesic-like features for point matching----Institute of Automation

Geodesic-like features for point matching

Dec 16, 2016Author：

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Title:

Geodesic-like features for point matching

Authors: Qian, DH; Chen, TS; Qiao, H

Author Full Names: Qian, Deheng; Chen, Tianshi; Qiao, Hong

Source:

NEUROCOMPUTING, 218 401-410; 10.1016/j.neucom.2016.08.092

DEC 19 2016

Language: English

Abstract: 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.

ISSN: 0925-2312

eISSN: 1872-8286

IDS Number:

EC3VA

Unique ID: WOS:000388053700044

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