Shift-map Image Registration

Shift-map image processing is a new framework based on energy minimization over a large space of labels. The optimization utilizes $\alpha$-expansion moves and iterative refinement over a Gaussian pyramid. In this paper we extend the range of applications to image registration. To do this, new data and smoothness terms have to be constructed. We note a great improvement when we measure pixel similarities with the dense \daisy\ descriptor. The main contributions of this paper are: * The extension of the shift-map framework to include image registration. We register images for which \sift\ only provides 3 correct matches. * The first publicly available implementation of shift-map image processing (e.g. inpainting, registration). We conclude by comparing shift-map registration to a recent method for optical flow with favorable results