Two-Dimensional PCA combined with PCA for neural network based image registration

Authors:
Anbang Xu;Xin Jin;Ping Guo
Affiliations:
Image Processing & Pattern Recognition Laboratory, Beijing Normal University, Beijing, China;Image Processing & Pattern Recognition Laboratory, Beijing Normal University, Beijing, China;Image Processing & Pattern Recognition Laboratory, Beijing Normal University, Beijing, China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Year:
2006

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Abstract

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.