Feature-based image registration in log-polar domain

Image registration is a necessary step in a variety of computer vision applications. One of the recent focus areas in image registration is extracting and matching features that are invariant to affine transformation. This is critical in various applications, including 3D reconstruction and object recognition. In this paper, we present a feature-based image registration method that is robust to scaling and rotation. This is achieved by extracting and matching features in the log-polar domain, where rotation and scale correspond to translation. Registration parameters are then estimated by applying the RANSAC technique to the feature correspondences. The RANSAC technique provides a robust estimation even when there are moving objects within the scene. Experimental results with synthetic and real images are provided.