This paper presents an energy minimization approach to registration of significantly dissimilar images, acquired by sensors of different modalities. The proposed algorithm introduces a robust matching criterion by aligning the locations of gradient maxima. The alignment is formulated as a parametric variational optimization problem which is solved iteratively by considering the intensities of a single image. The locations of the maxima of the second image's gradient are used as initialization. This implicit matching criterion is achieved while utilizing the full spatial information, without the need to use invariant image representations. We were able to robustly estimate affine and projective global motions using 'coarse to fine' processing, even when the images are characterized by complex space varying intensity transformations. These cause current state-ofthe- Art algorithms to fail. Finally, we present the registration of real images, which were taken by multi-sensor and multi-modality using affine and projective motion models.