Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment
Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment
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DOI：
10.1109/iccv.2007.4409180
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
D. Mateus;Fabio Cuzzolin;R. Horaud;Edmond Boyer
中科院分区：
文献类型：
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作者：
D. Mateus;Fabio Cuzzolin;R. Horaud;Edmond Boyer
在本文中，我们提出了一种匹配关节形状的方法，该方法通过对齐局部线性嵌入生成的对应的嵌入云来表示大的3D点集。特别地，我们证明了该问题等价于在作用于d维嵌入的正交变换下对齐两组点。该方法可以很好地被视为属于基于模型的集群框架，并且被实现为在估计数据点之间的对应性和估计最优对齐变换之间交替的EM算法。通过样本外扩展将一组数据点嵌入到另一组数据点上来初始化对应关系。给出了表示运动人员的体素集对的结果。提供了嵌入空间维度影响的经验证据，表明使用高维空间有助于在具有挑战性的真实世界场景中进行匹配，而不会对收敛产生附带影响。
In this paper we propose a method for matching articulated shapes represented as large sets of 3D points by aligning the corresponding embedded clouds generated by locally linear embedding. In particular we show that the problem is equivalent to aligning two sets of points under an orthogonal transformation acting onto the d-dimensional embeddings. The method may well be viewed as belonging to the model-based clustering framework and is implemented as an EM algorithm that alternates between the estimation of correspondences between data-points and the estimation of an optimal alignment transformation. Correspondences are initialized by embedding one set of data- points onto the other one through out-of-sample extension. Results for pairs of voxelsets representing moving persons are presented. Empirical evidence on the influence of the dimension of the embedding space is provided, suggesting that working with higher-dimensional spaces helps matching in challenging real-world scenarios, without collateral effects on the convergence.