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 invarian...

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Bibliographic Details

Published in:	2008 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 8
Main Authors:	Mateus, Diana, Horaud, Radu, Knossow, David, Cuzzolin, Fabio, Boyer, Edmond
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.06.2008
Subjects:	Clustering algorithms Eigenvalues and eigenfunctions Fellows Graph theory Histograms Kernel Laplace equations Noise shaping Shape Topology
ISBN:	9781424422425, 1424422426
ISSN:	1063-6919, 1063-6919
Online Access:	Get full text
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