Abstract:
Neural network-based image registration using global image features is relatively a new
research subject and the schemes devised so far use a feedforward neural network to
find the geometrical transformation parameters. In this work, we propose to use a radial
basis function neural network instead of feedforward neural network to overcome
lengthy pre-registration training stage. This modification has been tested on a typical
neural network-based registration method using discrete cosine transformation features
in the presence of noise. The proposed scheme does not only speed up the training stage
enormously, but also increases the accuracy and robustness against additive white noise
owing to the better generalization ability of the radial basis function neural networks.