[@Cordes2013] which provides accurate ground truth homographies for different scenes shown in Fig. \[fig:hannover\_dataset\]. Similar to the previous experiment, we first extract FAST corners [@Rosten2006] for each image. ORB descriptors [@Rublee2011] are then computed for every corner. Using image pairs between successive images in each scene, we obtain inlier point correspondences by first performing feature matching, followed by a verification step using the ground-truth homography.

For each inlier point correspondence, the ground-truth affine warp is estimated from the homography using the method of [@Barath2015]. All LPs are then trained using $m=5000$ sample warps. Further, we train the translational parameters to vary from -1.0 to 1.0, whereas the remaining parameters vary from -0.3 to 0.3.

The results from this experiment are shown in Table \[tab:hannover\_rmse\], where we only show results for image pairs with more than 50 point correspondences. For the energy-based methods, we find that ESM generally exhibits lower errors compared to IC-LK. Among all the methods, the results indicate that “symdct-25" and “dct-25" method are in general the best performing method, obtaining slightly lower error values compared to “sym" and “jd". This may be because some DCT coefficients corresponding to high frequency noise have been discarded. The sequences with 