Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Studies of model selection and regularization for generalization in neural networks with applications
Texture Classification Using Kernel Independent Component Analysis
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Modeling face appearance with nonlinear independent component analysis
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Isomap and neural networks based image registration scheme
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Regularization parameter estimation for feedforward neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we propose a new method to improve the image registration accuracy in feedforward neural networks (FNN) based scheme. In the proposed method, Bayesian regularization is applied to improve the generalization capability of the FNN. The features extracted from the image sets by kernel independent component analysis (KICA) technique are input vectors of regularized FNN. The outputs of the neural network are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments are performed between FNN with regularization and without regularization under various conditions. The results show that the proposed method is much improved not only at accuracy but also remarkably at robust to noise.