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Geometric and Photometric Data Fusion in Non-Rigid Shape Analysis

Published online by Cambridge University Press:  28 May 2015

Artiom Kovnatsky ,
Dan Raviv ,
Michael M. Bronstein ,
Alexander M. Bronstein  and
Ron Kimmel
Artiom Kovnatsky
Affiliation:
Institute of Computational Science, Faculty of Informatics, Università della Svizzera Italiana, Lugano, Switzerland
Dan Raviv
Affiliation:
Technion - Israel Institute of Technology, Computer Science Department, Haifa, Israel
Michael M. Bronstein*
Affiliation:
Institute of Computational Science, Faculty of Informatics, Università della Svizzera Italiana, Lugano, Switzerland
Alexander M. Bronstein
Affiliation:
School of Electrical Engineering, Tel Aviv University, Israel
Ron Kimmel
Affiliation:
Technion - Israel Institute of Technology, Computer Science Department, Haifa, Israel
*
Corresponding author.Email address:[email protected]
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Abstract

In this paper, we explore the use of the diffusion geometry framework for the fusion of geometric and photometric information in local and global shape descriptors. Our construction is based on the definition of a diffusion process on the shape manifold embedded into a high-dimensional space where the embedding coordinates represent the photometric information. Experimental results show that such data fusion is useful in coping with different challenges of shape analysis where pure geometric and pure photometric methods fail.

Keywords

65M1078A48Laplace-Beltrami operatordiffusion equationheat kernel descriptors3D shape retrievaldeformation invariance
Type
Research Article
Information
Numerical Mathematics: Theory, Methods and Applications , Volume 6 , Issue 1 , February 2013 , pp. 199 - 222
Copyright
Copyright © Global Science Press Limited 2013

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