Robust feature correspondences from a large set of unsorted wide baseline images

Given a set of unordered images taken in a wide area, an effective solution is proposed for establishing robust feature correspondences among them. Two major improvements are made in our work as follows: firstly, a robust technique is proposed for the self-organization of a large number of images without spatial orderings; secondly, a novel wide-baseline matching approach is developed to obtain good correspondences over images taken from substantially different viewpoints. The output consists of many sets of reliable pair-wise feature correspondences which are essential in various computer vision applications. Realistic experiments were carried out to evaluate the performances of the proposed method by using a large amount of images captured from our university's campus.