Pseudo-Polar Based Estimation of Large Translations Rotations and Scalings in Images

One of the major challenges related to image registration is the estimation of large motions without prior knowledge. This paper presents a Fourier based approach that estimates large translation, scale and rotation motions. The algorithm uses the pseudo-polar transform to achieve substantial improved approximations of the polor and log-polar Fourier transforms of an image. Thus, rotation and scale changes are reduced to translations which are estimated using phase correlation. By utilizing the pseudo-polar grid we increase the performance (accuracy, speed, robustness) of the registration algorithms. Scales up to 4 and arbitrary rotation angles can be robustly recovered, compared to a maximum scaling of 2 recovered by the current state-of-the-art algorithms. The algorithm utilizes only 1D FFT calculations whose overall complexity is significantly lower than prior works. Experimental results demosntrate the applicability of these algorithms.