Abstract
A novel image registration scheme is proposed. In the proposed scheme, the complete isometric mapping (Isomap) is used to extract features from the image sets, and these features are input vectors of feedforward neural networks. Neural network outputs are those translation, rotation and scaling parameters with respect to reference and observed image sets. Comparative experiments for Isomap based method, the discrete cosine transform (DCT) and Zernike moment are performed. The results show that the proposed scheme is not only accurate but also remarkably robust to noise.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xu, A., Guo, P. (2006). Isomap and Neural Networks Based Image Registration Scheme. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_71
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DOI: https://doi.org/10.1007/11760023_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34437-7
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