Geometric Expansion for Local Feature Analysis and Matching

We present a novel method for locating large amounts of local matches between images, with highly accurate localization. Point matching is one of the most fundamental tasks in computer vision, being used extensively in applications such as object detection, object tracking and structure from motion. The major challenge in point matching is to preserve large numbers of accurate matches between corresponding scene locations under different geometric and radiometric conditions, while keeping the number of false matches small. Recent publications have shown that applying the affine transformation model on local regions is a particularly suitable approach for point matching. Yet, affine invariant methods are not used extensively for two reasons First, because these methods are computationally demanding and second is that the derived affine estimations are with limited accuracy. In this work, we propose a novel method of region expansion that enhances region matches detected by any state-of-the-art method. The method is based on accurate estimation of affine transformations, which is used to predict matching locations beyond initially detected matches. We utilize the improved estimations of affine transformations in order to locally verify tentative matches in an efficient way. We systematically reject false matches, while improving the localization of correct matches that are usually rejected by state-of-the-art methods.