Image registration using feature points, Zernike moments and an M-estimator

This paper presents recent improvements in a feature based image registration technique. This image registration technique is based on finding a set of feature points in the two images and using these feature points for registration. This is done in four steps. In the first, images are filtered with the Mexican hat wavelet to obtain the feature point locations. In the second, the Zernike moments of neighbourhoods around the feature points are calculated and compared in the third step to establish correspondence between feature points in the two images and in the fourth the transformation parameters between images are obtained using an iterative least squares technique to eliminate outliers. The proposed method is illustrated with examples of images with partial overlap, differences in scale, various affine distortions and contamination with noise.