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2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2010)
San Francisco, CA, USA
June 13, 2010 to June 18, 2010
ISBN: 978-1-4244-6984-0
pp: 2839-2846
Lok Ming Lui , Department of Mathematics, Harvard University, Cambridge, MA, USA
Tsz Wai Wong , Department of Mathematics, UCLA, Los Angeles, CA, USA
Paul Thompson , Laboratory of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA
Tony Chan , Hong Kong University of Science and Technology, Hong Kong
Xianfeng Gu , Department of Computer Science, SUNY Stony Brook, Stony Brook, NY, USA
Shing-Tung Yau , Department of Mathematics, Harvard University, Cambridge, MA, USA
Surface registration is widely used in machine vision and medical imaging, where 1-1 correspondences between surfaces are computed to study their variations. Surface maps are usually stored as the 3D coordinates each vertex is mapped to, which often requires lots of storage memory. This causes inconvenience in data transmission and data storage, especially when a large set of surfaces are analyzed. To tackle this problem, we propose a novel representation of surface diffeomorphisms using Beltrami coefficients, which are complex-valued functions defined on surfaces with supreme norm less than 1. Fixing any 3 points on a pair of surfaces, there is a 1-1 correspondence between the set of surface diffeomorphisms between them and the set of Beltrami coefficients on the source domain. Hence, every bijective surface map can be represented by a unique Bel-trami coefficient. Conversely, given a Beltrami coefficient, we can reconstruct the unique surface map associated to it using the Beltrami Holomorphic flow (BHF) method introduced in this paper. Using this representation, 1/3 of the storage space is saved. We can further reduce the storage requirement by 90% by compressing the Beltrami coefficients using Fourier approximations. We test our algorithm on synthetic data, real human brain and hippocampal surfaces. Our results show high accuracy in the reconstructed data, while the amount of storage is greatly reduced. Our approach is compared with the Fourier compression of the coordinate functions using the same amount of data. The latter approach often shows jaggy results and cannot guarantee to preserve diffeomorphisms.

T. Chan, T. W. Wong, P. Thompson, X. Gu, L. M. Lui and S. Yau, "Compression of surface registrations using Beltrami coefficients," 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR), San Francisco, CA, USA, 2010, pp. 2839-2846.
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