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Proceedings Paper

Elastic surface registration by parameterization optimization in spectral space
Author(s): Fijoy Vadakkumpadan; Yunxia Tong; Yinlong Sun
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Paper Abstract

This paper proposes a novel method to register 3D surfaces. Given two surface meshes, we formulate the registration as a problem of optimizing the parameterization of one mesh for the other. The optimal parameterization of the mesh is achieved in two steps. First, we find an initial solution close to the optimal solution. Second, we elastically modify the parameterization to minimize the cost function. The modification of the parameterization is expressed as a linear combination of a relatively small number of low-frequency eigenvectors of an appropriate mesh Laplacian. The minimization of the cost function uses a standard nonlinear optimization procedure that determines the coefficients of the linear combination. Constraints are added so that the parameterization validity is preserved during the optimization. The proposed method extends parametric registration of 2D images to the domain of 3D surfaces. This method is generic and capable of elastically registering surfaces with arbitrary geometry. It is also very efficient and can be fully automatic. We believe that this paper for the first time introduces eigenvectors of mesh Laplacians into the problem of surface registration. We have conducted experiments using real meshes that represent human cortical surfaces and the results are promising.

Paper Details

Date Published: 2 February 2006
PDF: 9 pages
Proc. SPIE 6065, Computational Imaging IV, 606513 (2 February 2006); doi: 10.1117/12.643212
Show Author Affiliations
Fijoy Vadakkumpadan, Purdue Univ. (United States)
Yunxia Tong, Purdue Univ. (United States)
Yinlong Sun, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 6065:
Computational Imaging IV
Charles A. Bouman; Eric L. Miller; Ilya Pollak, Editor(s)

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