  Image registration (IR) is a fundamental task in image processing for
matching two or more images of the same scene taken at different times, from
different viewpoints and/or by different sensors. Due to the enormous diversity
of IR applications, automatic IR remains a challenging problem to this day. A
wide range of techniques has been developed for various data types and
problems. However, they might not handle effectively very large images, which
give rise usually to more complex transformations, e.g., deformations and
various other distortions.
  In this paper we present a genetic programming (GP)-based approach for IR,
which could offer a significant advantage in dealing with very large images, as
it does not make any prior assumptions about the transformation model. Thus, by
incorporating certain generic building blocks into the proposed GP framework,
we hope to realize a large set of specialized transformations that should yield
accurate registration of very large images.
