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.
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Xu, A., Jin, X., Guo, P. (2006). Two-Dimensional PCA Combined with PCA for Neural Network Based Image Registration. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_87
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DOI: https://doi.org/10.1007/11881223_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45907-1
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