Acceleration of GAT correlation for distortion-tolerant image matching

This paper describes an efficient acceleration of GAT (Global Affine Transformation) correlation as a powerful technique of distortion-tolerant image matching. The key ideas are twofold: efficient calculation of optimal affine parameters that maximize the normalized cross-correlation value between an input image and a template via separation of variables in the original GAT computational model and generation of lookup tables to further reduce the computational cost. Experiments using a template and its artificially affine-transformed images as input images demonstrate that the accelerated GAT correlation technique can absorb a fair amount of affine transformation much accurately than the well-known tangent distance method. Furthermore, the computation time of the accelerated GAT correlation is greatly reduced to less than five percent of that of the original GAT correlation. As a result, the processing time of the accelerated GAT correlation is around a half of that of the tangent distance method.