Kinematic Structure Correspondences Via Hypergraph Matching
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016)
摘要
In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching. In contrast to prior appearance and graph alignment based matching methods which have been applied among two similar static images, the proposed method finds correspondences between two dynamic kinematic structures of heterogeneous objects in videos. Our main contributions can be summarised as follows: (i) casting the kinematic structure correspondence problem into a hypergraph matching problem, incorporating multi-order similarities with normalising weights, (ii) a structural topology similarity measure by a new topology constrained subgraph isomorphism aggregation, (iii) a kinematic correlation measure between pairwise nodes, and (iv) a combinatorial local motion similarity measure using geodesic distance on the Riemannian manifold. We demonstrate the robustness and accuracy of our method through a number of experiments on complex articulated synthetic and real data.
更多查看译文
关键词
Riemannian manifold,geodesic distance,constrained subgraph isomorphism aggregation,structural topology similarity measure,graph alignment,kinematic structure correspondence,hypergraph matching
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络