We present a novel method for finding the optimal affine transformation for matching of images. The method requires no feature points, does not rely on normalization of images and can be tuned to highlight interesting parts in the images. Furthermore, the method does not need any derivatives for obtaining the affine transformation and it has a computational cost proportional to n/sup 2/logn for n/spl times/n images. The problem of finding the optimal affine transformation is solved by an iterative algorithm. In each step a global optimization is performed by the use of FFT. This global characteristic helps the algorithm from getting trapped in a local optimum. Novel theoretical results are presented that show under what restrictions the algorithm can be expected to work properly. Its intended primary use is for reconstruction problems in computer vision. These rely heavily on the establishment of point correspondences in the images. Since the method makes no assumptions on the images it can be used when feature points are difficult to detect. Experiments on real images are included and it is shown that the algorithm is robust and performs well even in difficult situations, with occlusions.
['Rikard Berthilsson']
Finding correspondences of patches by means of affine transformations