Bicycle Chain Shape Models

Stefan Horst Sommer; Aditya Jayant Tatu; Chen Chen; Dan Jørgensen; Marleen de Bruijne; Marco Loog; Mads Nielsen; Francois Bernard Lauze

doi:10.1109/CVPR.2009.5204053

Bicycle Chain Shape Models

Stefan Horst Sommer, Aditya Jayant Tatu, Chen Chen, Dan Jørgensen, Marleen de Bruijne, Marco Loog, Mads Nielsen, Francois Bernard Lauze

Department of Computer Science

7 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Number of pages	7
Publisher	IEEE
Publication date	2009
Pages	157-163
ISBN (Electronic)	978-1-4244-3992-2
DOIs	https://doi.org/10.1109/CVPR.2009.5204053
Publication status	Published - 2009
Event	CVPR 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - Miami Beach, United States Duration: 20 Jun 2009 → 25 Jun 2009

Conference

Conference	CVPR 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Country/Territory	United States
City	Miami Beach
Period	20/06/2009 → 25/06/2009

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10.1109/CVPR.2009.5204053

http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5204041&arnumber=5204053&count=149&index=30

Cite this

Bicycle Chain Shape Models. / Sommer, Stefan Horst; Tatu, Aditya Jayant; Chen, Chen et al.

Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.. IEEE, 2009. p. 157-163.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Sommer, SH, Tatu, AJ, Chen, C, Jørgensen, D, de Bruijne, M, Loog, M, Nielsen, M & Lauze, FB 2009, Bicycle Chain Shape Models. in Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.. IEEE, pp. 157-163, CVPR 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition., Miami Beach, United States, 20/06/2009. https://doi.org/10.1109/CVPR.2009.5204053

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title = "Bicycle Chain Shape Models",

abstract = "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.",

author = "Sommer, {Stefan Horst} and Tatu, {Aditya Jayant} and Chen Chen and Dan J{\o}rgensen and {de Bruijne}, Marleen and Marco Loog and Mads Nielsen and Lauze, {Francois Bernard}",

year = "2009",

doi = "10.1109/CVPR.2009.5204053",

language = "English",

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booktitle = "Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.",

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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

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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

T2 - CVPR 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Y2 - 20 June 2009 through 25 June 2009

ER -

Bicycle Chain Shape Models

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