Learning Varying Dimension Radial Basis Functions for Deformable Image Alignment

Date

2009

Authors

Yang, Di
Li, Hongdong

Journal Title

Journal ISSN

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

This paper presents a method for learning Radial Basis Functions (RBF) model with variable dimensions for aligning/registrating images of deformable surface. Traditional RBF-based approach, which is mainly based on a fixed dimension parametric model, often suffers from severe parameter over-fitting and complicated model selection (i.e. select the number and locations of centers determination) problems which lead to inaccurate estimation and unreliable convergence. Our strategy for solving both the parameter over-fitting and model selection problems is through the use of a probabilistic Bayesian inference model to obtain a posterior estimation of the alignment as well as the model parameters simultaneously. To learn the parameters of the Bayesian model, a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is employed, allowing us to handle large deformation image registration. Our approach is demonstrated successfully on real image sequences of different deformation types, with results compared favorable against other existing approaches.

Description

Keywords

Keywords: Bayesian inference model; Bayesian model; Deformable surfaces; Image alignment; Large deformations; Model parameters; Model Selection; Model selection problem; Overfitting; Parametric models; Radial basis functions; Real image sequences; Reversible jump M

Citation

Source

Proceedings of IEEE International Conference on Computer Vision (ICCV 2009)

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

10.1109/ICCVW.2009.5457681

Restricted until

2037-12-31