Two-Dimensional PCA Combined with PCA for Neural Network Based Image Registration

  • Anbang Xu
  • Xin Jin
  • Ping Guo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


A novel image registration scheme is proposed. In the proposed scheme, two-dimensional principal component analysis (2DPCA) combined with principal component analysis (PCA) is used to extract features from the image sets and these features are fed into feedforward neural networks to provide translation, rotation and scaling parameters. Comparison experiments between 2DPCA combined with PCA based method and the other two former methods: discrete cosine transform (DCT) and Zernike moment, are performed. The results indicate that the proposed scheme is both accurate and remarkably robust to noise.


Principal Component Analysis Discrete Cosine Transform Image Registration Feedforward Neural Network Zernike Moment 
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
  • Xin Jin
    • 1
  • Ping Guo
    • 1
  1. 1.Image Processing & Pattern Recognition LaboratoryBeijing Normal UniversityBeijingChina

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