Learning Varying Dimension Radial Basis Functions for Deformable Image Alignment
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of IEEE International Conference on Computer Vision (ICCV 2009)|
|01_Yang_Learning_Varying_Dimension_2009.pdf||491.28 kB||Adobe PDF||Request a copy|
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