Magnitude Type Preserving Similarity Measure for Complex Wavelet Based Image Registration
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.
KeywordsSimilarity Measure Reference Image Image Registration Wavelet Coefficient Type Divergence
Unable to display preview. Download preview PDF.
- 3.Berthilsson, R.: Affine Correlation in International Conference on Pattern Recognition, Brisbane, Qld, pp. 1458–1461 (1998)Google Scholar
- 8.Shapiro, L.G., Stockman, G.C.: Computer Vision. Prentice Hall, Upper Saddle River (2001)Google Scholar
- 9.Viola, P.A.: Alignment by Maximization of Mutual Information. Ph.D. thesis, Massachusetts Institute of Technology (1995)Google Scholar
- 11.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)Google Scholar
- 12.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 Google Scholar
- 14.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)Google Scholar