Graph-based multiple panorama extraction from unordered image sets

This paper presents a multi-image registration method, which aims at recognizing and extracting multiple panoramas from an unordered set of images without user input. A method for panorama recognition introduced by Lowe and Brown [ I] is based on extraction of a full set of scale invariant image features and fast matching in feature space, followed by post-processing procedures. We propose a different approach, where the full set of descriptors is not required, and a small number of them are used to register a pair of images. We propose feature point indexing based on corner strength value. By matching descriptor pairs with similar corner strengths we update clusters in rotation-scale accumulators, and a probabilistic approach determines when these clusters are further processed with RANSAC to find inliers of image homography. If the number of inliers and global similarity between images are sufficient, a fast geometry-guided point matching is performed to improve the accuracy of registration. A global registration graph, whose node weights are proportional to the image similarity in the area of overlap, is updated with each new registration. This allows the prediction of undiscovered image registrations by finding the shortest paths and corresponding transformation chains. We demonstrate our approach using typical image collections containing multiple panoramic sequences.