An improved SIFT operator-based image registration using cross-correlation information

SIFT operator-based registration methods have been found efficient matches between features with unique local neighborhoods, yet they fail to handel global context to resolve ambiguities that can occur locally when images to be registered have similar regions. This paper presents an novel image registration method, based on local SIFT operator and cross-correlation information. The cross-correlation information can discriminate local features that have similar local appearance. Moreover, the proposed image registration algorithm has better robust performance in matching under noise environments. Simulation results confirm that the proposed algorithm can give better image match than two related image registration algorithms under noise environments.