Groupwise Non-Rigid Image Alignment - Results of a Multiscale Iterative Best Edge Algorithm

In this paper we present an algorithm for groupwise image alignment using an iterative best edge point algorithm. Neighbouring image edge points are matched for similarity in a two directional fashion. The matches found are used to drive a regularised warp of the images into alignment. The algorithm works from low to high resolution, with the matches calculated across the set first at low resolution and towards progressively finer scales. The regularisation decreases across iterations, and the search area remains constant, so covers larger effective area in the low resolution images. We also extend the method to 3D surfaces by combining the 2D image search with a 3D ICP algorithm. The results show that this gives a very efficient algorithm that can align many different sets of 2D images and 3D surfaces.