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A new fast feature-based approach for efficient and accurate automated image registration with applications to multiple-views or Multi-sensor LADAR imaging is presented. As it is known, highly accurate and efficient Image registration is highly needed and desired in ground or Airborne LADAR imaging. The proposed approach is two-fold: First, direct comparison of sub-image patches of the overlapping images is performed applying the normalized cross-correlation technique. A pre-specified window sub-image patch size is used to speed up the matching process. In particular, a 65x65 window is defined in the right 50% of the left image (reference image) then, a matching window in the left 50% of the right image (unregistered image) is searched. The beauty of this approach is that the original images are reduced to small and similar sub-image patches of size 65x65, reducing tremendously the computation time of the matching point pairs search process, which as we show, speeds up tremendously the derivation of the matching points pairs described in the next phase. Second, Wavelet transform is applied then to the small and similar sub-image patches to extract a number of matching feature points. Each feature point is an edge point whose edge response is the maximum within a neighborhood. The normalized cross correlation technique is applied again this time to find the matching pairs between the feature points. From the matching pairs, the moments of inertia are applied to estimate the rigid transformation parameters between the overlapping images. In general, the overlapping images can have an arbitrarily large orientation difference. Therefore, this angle must be found first to correct the unregistered image. In order to estimate the rotation angle, we show how a so-called "angle histogram" is derived and calculated. The rotation angle selected is the one that corresponds to the maximum
peak in the angle histogram. We show how the proposed approach is of an order of magnitude faster than the existing methods, on a single-processor computer. We show also that the proposed approach is automatic, robust, and can work with any partially overlapping images rotated from each other. Experimental results using rotated and non-rotated images are presented.
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