Articulated shape matching using Laplacian eigenfunctions and unsupervised point registration

Matching articulated shapes represented by voxel-sets reduces to maximal sub-graph isomorphism when each set is described by a weighted graph. Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings in virtue of their invariance to change of pose. Classical graph isomorphism schemes relying on the ordering of the eigenvalues to align the eigenspaces fail when handling large data-sets or noisy data. We derive a new formulation that finds the best alignment between two congruent K-dimensional sets of points by selecting the best subset of eigenfunctions of the Laplacian matrix. The selection is done by matching eigenfunction signatures built with histograms, and the retained set provides a smart initialization for the alignment problem with a considerable impact on the overall performance. Dense shape matching casted into graph matching reduces then, to point registration of embeddings under orthogonal transformat...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Diana Mateus, Radu Horaud, David Knossow, Fabio Cuzzolin, Edmond Boyer
• Keywords: Computer Vision, CVPR 2008, Graph Isomorphism Schemes, Graph Matching Reduces, Non Identical Shapes, Spectral Graph Theory, Weighted Graph