Shape Modelling Using Markov Random Field Restoration of Point Correspondences
- 1 January 2003
- book chapter
- Published by Springer Nature
- Vol. 18, 1-12
- https://doi.org/10.1007/978-3-540-45087-0_1
Abstract
A method for building statistical point distribution models is proposed. The novelty in this paper is the adaption of Markov random field regularization of the correspondence field over the set of shapes. The new approach leads to a generative model that produces highly homogeneous polygonized shapes and improves the capability of reconstruction of the training data. Furthermore, the method leads to an overall reduction in the total variance of the point distribution model. Thus, it finds correspondence between semi-landmarks that are highly correlated in the shape tangent space. The method is demonstrated on a set of human ear canals extracted from 3D-laser scans.Keywords
This publication has 15 references indexed in Scilit:
- Statistical shape analysis using non-Euclidean metricsMedical Image Analysis, 2003
- Brownian Warps: A Least Committed Prior for Non-rigid RegistrationPublished by Springer Nature ,2002
- Building and Testing a Statistical Shape Model of the Human Ear CanalPublished by Springer Nature ,2002
- 3D Statistical Shape Models Using Direct Optimisation of Description LengthPublished by Springer Nature ,2002
- An Information Theoretic Approach to Statistical Shape ModellingPublished by British Machine Vision Association and Society for Pattern Recognition ,2001
- Surface-bounded growth modeling applied to human mandiblesIEEE Transactions on Medical Imaging, 2000
- Non-rigid Registration by Geometry-Constrained DiffusionPublished by Springer Nature ,1999
- Shape and the Information in Medical Images: A Decade of the Morphometric SynthesisComputer Vision and Image Understanding, 1997
- Active Shape Models-Their Training and ApplicationComputer Vision and Image Understanding, 1995
- Generalized Procrustes AnalysisPsychometrika, 1975