KEYPOINTS DICTIONARY LEARNING FOR FAST AND ROBUST ALIGNMENT

Sparse keypoints based methods allow to match two images in an efficient manner. However, even though they are sparse, not all generated keypoints are necessary. This uselessly increases the computational cost during the matching step and can even add uncertainty when these keypoints are not discriminatory enough, thus leading to imprecise, or even wrong, alignment. In this paper, we address the important case where the alignment deals with the same scene or the same type of object. This enables a preliminary learning of optimal keypoints, in terms of efficiency and robustness. Our fully unsupervised selection method is based on a statistical a contrario test on a small set of training images to build without any supervision a dictionary of the most relevant points for the alignment. We show the usefulness of the proposed method on two applications, the stabilization of video surveillance sequences and the fast alignment of industrial objects containing repeated patterns. Our experiments demonstrate an acceleration of the method by 20 factor and significant accuracy gain.