DocumentCode
254492
Title
Surface Registration by Optimization in Constrained Diffeomorphism Space
Author
Wei Zeng ; Lok Ming Lui ; Xianfeng Gu
Author_Institution
Florida Int. Univ., Miami, FL, USA
fYear
2014
fDate
23-28 June 2014
Firstpage
4169
Lastpage
4176
Abstract
This work proposes a novel framework for optimization in the constrained diffeomorphism space for deformable surface registration. First the diffeomorphism space is modeled as a special complex functional space on the source surface, the Beltrami coefficient space. The physically plausible constraints, in terms of feature landmarks and deformation types, define subspaces in the Beltrami coefficient space. Then the harmonic energy of the registration is minimized in the constrained subspaces. The minimization is achieved by alternating two steps: 1) optimization - diffuse the Beltrami coefficient, and 2) projection - first deform the conformal structure by the current Beltrami coefficient and then compose with a harmonic map from the deformed conformal structure to the target. The registration result is diffeomorphic, satisfies the physical landmark and deformation constraints, and minimizes the conformality distortion. Experiments on human facial surfaces demonstrate the efficiency and efficacy of the proposed registration framework.
Keywords
conformal mapping; image registration; minimisation; Beltrami coefficient space; complex functional space; conformality distortion; constrained diffeomorphism space; deformable surface registration; deformed conformal structure; harmonic energy; human facial surfaces; physically plausible constraints; Conformal mapping; Equations; Harmonic analysis; Measurement; Optimization; Surface morphology; Topology; Diffeomorphism Space; Optimization; Physical Constraints; Quasiconformal Map; Surface Registration;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on
Conference_Location
Columbus, OH
Type
conf
DOI
10.1109/CVPR.2014.531
Filename
6909927
Link To Document