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Image Registration with Regularized Neural Network

  • Anbang Xu
  • Ping Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

Abstract

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.

Keywords

Discrete Cosine Transform Image Registration Feedforward Neural Network Registration Accuracy Noisy Condition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anbang Xu
    • 1
  • Ping Guo
    • 1
  1. 1.Image Processing Pattern Recognition LaboratoryBeijing Normal UniversityBeijingP.R. China

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