A computational vision approach to image registration

Automatic image registration is important for many multiframe-based image analysis applications. In this paper, a computational vision approach is presented for the estimation of 2-D translation, rotation, and scale from two partially overlapping images. The approach has the following features: (1) an illuminant direction estimator is used to obtain an initial estimate of camera rotation; (2) feature points are located based on a Gabor wavelet model for detecting local curvature discontinuities; (3) estimation of rotation and translation is formulated as a linear problem; and (4) hierarchical coarse-to-fine matching is used. This results in a fast and novel algorithm that produces good results even when large rotations and scale changes have occurred between the two frames and the images are devoid of significant features. Several applications of the algorithm are presented