Bicycle Chain Shape Models
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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Bicycle Chain Shape Models. / Sommer, Stefan Horst; Tatu, Aditya Jayant; Chen, Chen; Jørgensen, Dan; de Bruijne, Marleen; Loog, Marco; Nielsen, Mads; Lauze, Francois Bernard.
Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.. IEEE, 2009. s. 157-163.Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
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TY - GEN
T1 - Bicycle Chain Shape Models
AU - Sommer, Stefan Horst
AU - Tatu, Aditya Jayant
AU - Chen, Chen
AU - Jørgensen, Dan
AU - de Bruijne, Marleen
AU - Loog, Marco
AU - Nielsen, Mads
AU - Lauze, Francois Bernard
PY - 2009
Y1 - 2009
N2 - In this paper we introduce landmark-based preshapes which allow mixing of anatomical landmarks and pseudo-landmarks, constraining consecutive pseudo-landmarks to satisfy planar equidistance relations. This defines naturally a structure of Riemannian manifold on these preshapes, with a natural action of the group of planar rotations. Orbits define the shapes. We develop a Geodesic Generalized Procrustes Analysis procedure for a sample set on such a preshape spaces and use it to compute Principal Geodesic Analysis. We demonstrate it on an elementary synthetic example as well on a dataset of manually annotated vertebra shapes from X-ray. We re-landmark them consistently and show that PGA captures the variability of the dataset better than its linear counterpart, PCA.
AB - In this paper we introduce landmark-based preshapes which allow mixing of anatomical landmarks and pseudo-landmarks, constraining consecutive pseudo-landmarks to satisfy planar equidistance relations. This defines naturally a structure of Riemannian manifold on these preshapes, with a natural action of the group of planar rotations. Orbits define the shapes. We develop a Geodesic Generalized Procrustes Analysis procedure for a sample set on such a preshape spaces and use it to compute Principal Geodesic Analysis. We demonstrate it on an elementary synthetic example as well on a dataset of manually annotated vertebra shapes from X-ray. We re-landmark them consistently and show that PGA captures the variability of the dataset better than its linear counterpart, PCA.
U2 - 10.1109/CVPR.2009.5204053
DO - 10.1109/CVPR.2009.5204053
M3 - Article in proceedings
SP - 157
EP - 163
BT - Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
PB - IEEE
Y2 - 20 June 2009 through 25 June 2009
ER -
ID: 12872680