Keypoint matching and image registration using sparse representations
The field of sparse representations has found applications in a variety of computer vision and scientific fields. Although sparse representations were initially considered for reconstruction, they have been successfully adapted for classification. In this paper, we demonstrate the application of sparse representations in matching salient keypoint descriptors for image alignment, registration, or stitching. Our method initially builds a dictionary from keypoints in a reference image. Then keypoints associated with one or more secondary images are sparsely represented using the reference dictionary. The sparse coefficient signatures are analyzed to determine if there is a matched pair and identify the reference keypoint of the match. Top keypoint matches are used to construct the homography transformation for image registration. The usefulness of our methodology is demonstrated across several types of imagery showing robust performance while delivering state-of-the-art image alignment and registration.