Alignment using distributions of local geometric properties

We describe a framework for aligning images without needing to establish explicit feature correspondences. We assume that the geometry between the two images can be adequately described by an affine transformation and develop a framework that uses the statistical distribution of geometric properties of image contours to estimate the relevant transformation parameters. The estimates obtained using the proposed method are robust to illumination conditions, sensor characteristics, etc., since image contours are relatively invariant to these changes. Moreover, the distributional nature of our method alleviates some of the common problems due to contour fragmentation, occlusion, clutter, etc. We provide empirical evidence of the accuracy and robustness of our algorithm. Finally, we demonstrate our method on both real and synthetic images, including multisensor image pairs.