Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment

Diana Mateus (1) , Fabio Cuzzolin (1) , Radu Horaud (1) , Edmond Boyer (1)

1 PERCEPTION - Interpretation and Modelling of Images and Videos

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.

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]

Diana Mateus, Fabio Cuzzolin, Radu Horaud, Edmond Boyer. Articulated Shape Matching Using Locally Linear Embedding and Orthogonal Alignment. NRTL 2007 - Workshop on Non-rigid Registration and Tracking through Learning, Oct 2007, Rio de Janeiro, Brazil. pp.1-8, ⟨10.1109/ICCV.2007.4409180⟩. ⟨inria-00590237⟩