Bicycle Chain Shape Models

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 languageEnglish
Title of host publicationComputer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
Number of pages7
PublisherIEEE
Publication date2009
Pages157-163
ISBN (Electronic)978-1-4244-3992-2
DOIs
Publication statusPublished - 2009
EventCVPR 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - Miami Beach, United States
Duration: 20 Jun 200925 Jun 2009

Conference

ConferenceCVPR 2009. IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
LandUnited States
ByMiami Beach
Periode20/06/200925/06/2009

ID: 12872680