FAST GLOBAL IMAGE REGISTRATION USING RANDOM PROJECTIONS

We describe a new method for efficiently computing the global optimum of least squares registration problems based on the recently developed theory of signal processing using random projections. The method is especially attractive for large scale registration problems where the goal is to register many images to a standard template. We test our new algorithm using real images of cells´ nuclei and show our method can outperform more traditional dimension reduction methods such as projections onto lower dimensional B-spline function spaces