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Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation
Bernard, Florian; Gemmar, Peter; Hertel, Frank et al.
2016In Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation
Peer reviewed
 

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Abstract :
[en] Representing 3D shape deformations by linear models in high-dimensional space has many applications in computer vision and medical imaging, such as shape-based interpolation or segmentation. Commonly, using Principal Components Analysis a low-dimensional (affine) subspace of the high-dimensional shape space is determined. However, the resulting factors (the most dominant eigenvectors of the covariance matrix) have global support, i.e. changing the coefficient of a single factor deforms the entire shape. In this paper, a method to obtain deformation factors with local support is presented. The benefits of such models include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. For that, based on a well-grounded theoretical motivation, we formulate a matrix factorisation problem employing sparsity and graph-based regularisation terms. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation ability and sparse shape reconstruction, whereas for human body shapes our method gives more realistic deformations.
Disciplines :
Computer science
Author, co-author :
Bernard, Florian ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Gemmar, Peter
Hertel, Frank ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Goncalves, Jorge ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Thunberg, Johan ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
External co-authors :
yes
Language :
English
Title :
Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation
Publication date :
2016
Event name :
IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Event date :
from 26-06-2016 to 01-07-2016
Main work title :
Linear Shape Deformation Models with Local Support using Graph-based Structured Matrix Factorisation
Peer reviewed :
Peer reviewed
Funders :
Fonds National de Recherche (6538106, 8864515)
Available on ORBilu :
since 22 July 2016

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