<title>Using trilinearity to reduce misregistration in image mosaicing</title>

Abstract

Image mosaicing has been collecting widespread attention because it can automatically construct a panoramic image from multiple images. Among previous methods, homography-based methods are the most accurate in the geometric sense. This is because these methods use planar projective transformation, which considers perspective effects as a geometric transformation model between images. These methods, however, have a problem of misregistration in the case of general scenes with arbitrary camera motion. We propose a method that can reduce this misregistration by using geometric constraints called trilinearity. Trilinearity is a geometric relationship among three images taken from different viewpoints. By using this relationship, several techniques have already been introduced for other purposes, such as 3D shape recovery or motion analysis. We use this relationship for image mosaicing. The proposed method consists of the following three steps. First, it establishes feature correspondences among three images. We use small rectangular regions such as corners as features. Second, it computes the trilinearity from the feature correspondences. We use a robust method to exclude false correspondences. Third, it generates a panoramic image mosaic by using the trilinearity. Experiments using real images confirm the effectiveness of our method.