Abstract
Most of the similarity measures, currently used for image registration, aim to model the relation between the intensities of correspondent pixels. This is true even for some of the similarity measures that are not directly defined on the intensities of the images to be registered, but on some transformed version of those images. A potential problem with this approach is that it relies on the values of intensities, for which in most of the times it is too difficult to predict their pattern of variation. A way to circumvent this problem is to define a similarity measure on the domain of the magnitudes of complex wavelet coefficients, as the magnitudes are less affected by noise than the intensities. This property of robustness to noise allows to predict a certain behavior of the corresponding magnitudes, namely that they will preserve their type. This means that large (small) magnitudes from the complex wavelet transform of one image will correspond to large (small) magnitudes in the complex wavelet transform of the other image. Starting from this constancy in the behavior of complex wavelet magnitudes, we propose a new similarity measure that has sub pixel accuracy, robustness to noise and is faster than the most related known similarity measure.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21(11), 977–1000 (2003)
Goshtasby, A.: Image Registration. Principles, Tools and Methods. Springer, London (2012)
Berthilsson, R.: Affine Correlation in International Conference on Pattern Recognition, Brisbane, Qld, pp. 1458–1461 (1998)
Kaneko, S., Satoh, Y., Igarashi, S.: Using selective correlation coefficient for robust image registration. Pattern Recognition 36, 1165–1173 (2003)
Brown, L.G.: A survey of image registration techniques. ACM Computing Surveys 24(4), 325–376 (1992)
Venot, A., Devaux, J.Y., Herbin, M., Lebruchec, J.F., Dubertret, L., Raulo, Y., Roucayrol, J.C.: An automated system for the registration and comparison of photographic images in medicine. IEEE Trans. Med. Imaging 7(4), 298–303 (1988)
Conners, R.W., Harlow, C.A.: A theoretical comparison of texture algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 2(3), 204–222 (1980)
Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, Upper Saddle River (2001)
Viola, P.A.: Alignment by Maximization of Mutual Information. Ph.D. thesis, Massachusetts Institute of Technology (1995)
Collignon, A., Vandermeulen, A., Suetens, P., Marchal, G.: 3D multi-modality medical image registration based on information theory. Computational Imaging and Vision 3, 263–274 (1995)
Wachowiak, M.P., Smolikova, R., Tourassi, G.D., Elmaghraby, A.S.: Similarity metrics based on nonadditive entropies for 2D-3D multimodal biomedical image registration. In: Medical Imaging Conf., Proc. SPIE, San Diego, CA, vol. 5032, pp. 1090–1100 (2003)
Shannon, C.E.: The mathematical theory of communication. In: Shannon, C.E., Weaver, W. (eds.) The Mathematical Theory of Communication, pp. 29–125. University of Illinois Press, Urbana (1949); reprint 1998
Calnegru, F.-C.: A Probabilistic Framework for Complex Wavelet Based Image Registration. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011, Part I. LNCS, vol. 6978, pp. 9–18. Springer, Heidelberg (2011)
Calnegru, F.: Hidden State Probabilistic Modeling for Complex Wavelet Based Image Registration. In: International Conference on Computer Vision and Image Processing, pp. 1226–1232 (2011)
Kullback, S.: Information theory and statistics. John Wiley and Sons, New York (1959)
Selesnick, I.W., Barniuk, R.G., Kingsbury, N.G.: The Dual-Tree Complex Wavelet Transform. IEEE Signal Processing Magazine 22(6), 123–151 (2005)
Skerl, D., Likar, B., Pernus, P.: A Protocol for Evaluation of Similarity Measures for Rigid Registration. IEEE Transactions on Medical Imaging 25(6), 779–791 (2006)
Matthäus, L., Trillenberg, P., Fadini, T., Finke, M., Schweikard, A.: Brain mapping with transcranial magnetic stimulation using a refined correlation ratio and Kendall’s τ. Stat. Med. 27, 5252–5270 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calnegru, FC. (2013). Magnitude Type Preserving Similarity Measure for Complex Wavelet Based Image Registration. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-02895-8_10
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02894-1
Online ISBN: 978-3-319-02895-8
eBook Packages: Computer ScienceComputer Science (R0)