Image registration based on generalized and mean Hausdorff distances

A new method to register two un-identical imaging representation of the same objects based on generalized and mean Hausdorff distances is proposed. Firstly two image contours are registered by minimizing mean Hausdorff distance used as cost function, through two-dimensional translation and rotation with simulate anneal algorithm. The registration is often inaccurate due to the location-independent difference of two contours. Generalized Hausdorff distance was analyzed to ascertain the excess of the floating image over the model image. Then the new floating image subtracted the excess points is registered to the model image. Accurate registration was attained after several iterations.