Abstract
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 detail- subband 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 detail- subband correlation is unstable to periodic translations, and the degradation of subband correlation is mainly attributable to the translation sensitivity.
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