Abstract
This paper presents new methods that have been developed for registration of gray scale image, these methods are based on improve the registration precision and the ability of anti-noise, the image registration process was based on analyzed the situation of the point set of histogram, and defined the formula of the histogram divergence. To speed up searching the registration parameters, all were done in the wavelet field and a hybrid algorithm based on genetic algorithm and Powell’s method was used to optimize this parameters. Experimental results proved the algorithm can apply wider optimization methods and have better anti-noise robustness performance.
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References
Zitova, B., Flusser, J.: Image registration methods:a survey. Image and Vision Computing 21, 977–1000 (2003)
Pass, G., Zabih, R.: Comparing images using joint histograms (3) (1999)
West, J., Fitzpatrick, J.M., Wang, M.Y., et al.: Comparison and evaluation of retrospective inter modality brain image registration techniques. Journal of Computer Assisted Tomography 21(4), 554–566 (1997)
LeMoigne, J., Campbell, W.J., Cromp, R.F.: An automated parallel image registration technique based on the correlation of wavelet freatures. IEEE Transactions on 40(8), 1849–1864 (2002)
Chen, G., Wang, X., Zhuang, Z., et al.: Genetic Algorithms and Applications. People Post Press, Beijing (1996)
Tang, H., Qin, X.: Practical Methods of Optimization. Dalian University of Technology Press, Dalian (2001)
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© 2011 Springer-Verlag Berlin Heidelberg
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Xin, C., Niu, B. (2011). Image Registration Algorithm Based on Regional Histogram Divergence and Wavelet Decomposition. In: Wang, Y., Li, T. (eds) Knowledge Engineering and Management. Advances in Intelligent and Soft Computing, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25661-5_83
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DOI: https://doi.org/10.1007/978-3-642-25661-5_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25660-8
Online ISBN: 978-3-642-25661-5
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