The registrations of functions and images is a widely-studied problem
that has seen a variety of solutions in the recent years. Most of these solutions
are based on objective functions that fail to satisfy two most basic and desired
properties in registration: (1) invariance under identical warping: since the registration
between two images is unchanged under identical domain warping, the
cost function evaluating registrations should also remain unchanged; (2) inverse
consistency: the optimal registration of image A to B should be the same as that
of image B to A. We present a novel registration approach that uses the L2 norm,
between certain vector fields derived from images, as an objective function for
registering images. This framework satisfies symmetry and invariance properties.
We demonstrate this framework using examples from different types of images
and compare performances with some recent methods.