We investigate the correlation characteristics of multiresolution image representations using compactly supported Daubechies wavelets. Subband correlation of two images consists of crosscorrelation of respective low-pass (approximation) and high-pass (detail) subbands generated from wavelet transform. Based on subband correlation, a fast algorithm for target localization and image registration is provided. In particular, a peak of approximation-subband correlation at a resolution level provides a guide for candidate location at the next finer resolution level, thereby confining the searching region and accelerating the matching. The presence of a sharp correlation peak in detailsubband correlation provides a precise location. Using N-order Daubechies wavelets with N (1,2,3,4,5), the effects of the following aspects on subband correlation are investigated: (1) support length of wavelet filters, (2) random noises and static clutter, and (3) the in-plane rotation. Simulations show that approximation-subband correlation is relatively stable to random noises and static clutter whereas the detailsubband correlation is unstable to periodic translations, and the degradation of subband correlation is mainly attributable to the translation sensitivity.
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