Trainable method of parametric shape description
Image and Vision Computing - Special issue: BMVC 1991
A Minimum Description Length Approach to Statistical Shape Modelling
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Nonlinear Generative Models for Dynamic Shape and Dynamic Appearance
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Implicit, view invariant, linear flexible shape modelling
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
L1 Generalized Procrustes 2D Shape Alignment
Journal of Mathematical Imaging and Vision
A family of principal component analyses for dealing with outliers
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Functional 2d procrustes shape analysis
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
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When observing the 3D world through a 2D projection, rigid 3D rotation will result in an apparent deformation not accounted for in traditional shape analysis methodologies, e.g. those based on Generalized Procrustes Alignment and Principal Component Analysis. We propose using a 3D statistical model to infer relative depth to a 2D shape and consequently model the apparent deformation in a Procrustes alignment framework. We test our approach on vertebra shapes and show that it leads to a more compact and generalizable shape model, as well as to improvement in vertebra fracture prediction.