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Learning Varying Dimension Radial Basis Functions for Deformable Image Alignment

Yang, Di; Li, Hongdong


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...[Show more]

CollectionsANU Research Publications
Date published: 2009
Type: Conference paper
Source: Proceedings of IEEE International Conference on Computer Vision (ICCV 2009)
DOI: 10.1109/ICCVW.2009.5457681


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