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This paper presents an approach to the registration of significantly dissimilar images, acquired by sensors of different modalities. A robust matching criterion is derived by aligning the locations of gradient maxima. The alignment is achieved by iteratively maximizing the magnitudes of the intensity gradients of a set of pixels in one of the images, where the set is initialized by the gradient maxima locations of the second image. No explicit similarity measure that uses the intensities of both images is used. The computation utilizes the full spatial information of the first image and the accuracy and robustness of the registration depend only on it. False matchings are detected and adaptively weighted using a directional similarity measure. By embedding the scheme in a "coarse to fine” formulation, we were able to estimate affine and projective global motions, even when the images were characterized by complex space varying intensity transformations. The scheme is especially suitable when one of the images is of considerably better quality than the other (noise, blur, etc.). We demonstrate these properties via experiments on real multisensor image sets.
Global motion estimation, multisensor registration, multimodality image alignment.

A. Averbuch and Y. Keller, "Multisensor Image Registration via Implicit Similarity," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 28, no. , pp. 794-801, 2006.
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