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Linear shape deformation models with local support using graph-based structured matrix factorisation

Bernard, Florian ; Gemmar, Peter ; Hertel, Frank ; Goncalves, Jorge and Thunberg, Johan LU (2016) 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 p.5629-5638
Abstract
Representing 3D shape deformations by high-dimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional 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. Based on matrix factorisation with sparsity and graph-based regularisation terms, we present a method to obtain deformation factors with local support. The benefits include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. We demonstrate that for... (More)
Representing 3D shape deformations by high-dimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional 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. Based on matrix factorisation with sparsity and graph-based regularisation terms, we present a method to obtain deformation factors with local support. The benefits include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation and sparse reconstruction, whereas for body shapes our method gives more realistic deformations. (Less)
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author
; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
pages
10 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
conference location
Las Vegas, United States
conference dates
2016-06-26 - 2016-07-01
external identifiers
  • scopus:84986255641
ISBN
978-1-4673-8851-1
DOI
10.1109/CVPR.2016.607
language
English
LU publication?
no
id
18ce1f55-b4f0-4425-bd42-123fa81328a9
date added to LUP
2024-09-05 14:19:20
date last changed
2025-04-04 15:14:09
@inproceedings{18ce1f55-b4f0-4425-bd42-123fa81328a9,
  abstract     = {{Representing 3D shape deformations by high-dimensional linear models has many applications in computer vision and medical imaging. Commonly, using Principal Components Analysis a low-dimensional 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. Based on matrix factorisation with sparsity and graph-based regularisation terms, we present a method to obtain deformation factors with local support. The benefits include better flexibility and interpretability as well as the possibility of interactively deforming shapes locally. We demonstrate that for brain shapes our method outperforms the state of the art in local support models with respect to generalisation and sparse reconstruction, whereas for body shapes our method gives more realistic deformations.}},
  author       = {{Bernard, Florian and Gemmar, Peter and Hertel, Frank and Goncalves, Jorge and Thunberg, Johan}},
  booktitle    = {{Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}},
  isbn         = {{978-1-4673-8851-1}},
  language     = {{eng}},
  pages        = {{5629--5638}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Linear shape deformation models with local support using graph-based structured matrix factorisation}},
  url          = {{http://dx.doi.org/10.1109/CVPR.2016.607}},
  doi          = {{10.1109/CVPR.2016.607}},
  year         = {{2016}},
}