IEEE International Conference on Acoustics, Speech & Signal
Processing, vol. II, pp. 573-576, Philadelphia,
PA, March 2005
Translation Insensitive
Image Similarity in Complex Wavelet Domain
Zhou
Wang and Eero P. Simoncelli
Laboratory for Computational Vision,
PDF file for ICASSP poster (2.34M)
Abstract: We
propose a complex wavelet domain image similarity measure, which is
simultaneously insensitive to luminance change, contrast change and spatial
translation. The key idea is to make use of the fact that these image
distortions lead to consistent magnitude and/or phase changes of local wavelet
coefficients. Since small scaling and rotation of images can be locally
approximated by translation, the proposed measure also shows robustness to
spatial scaling and rotation when these geometric distortions are small
relative to the size of the wavelet filters. Compared with previous methods,
the proposed measure is computationally efficient, and can evaluate the
similarity of two images without a precise registration process at the front
end.