Exploiting Structural Constraints in Image Pairs

Abstract

Two images of a scene can provide the 3-dimensional structural information that is absent in a single 2-D image. This is because, provided correspondence can be established across the two views, the variations between the two images provide cues related to the depth ordering of objects in the scene. These cues can be exploited for applications such as 3-D reconstruction, mosaicing and computation of relative camera positions. While these applications are dependent upon the quality of the inter-image correspondence, with the anticipated correspondence noise having a significant impact on the problem formulation, many of these applications can also facilitate the correspondence computation. In this thesis, we explore the interlocking relationship between image correspondence and computation and utilization of structural cues using a series of case studies. In chapter 2, we show how studying the small motion problem with an explicit focus on the types of correspondence noise anticipated, allows for a theoretical fusion of the discrete and differential algorithms. In chapter 3, we consider how to design a structure from motion algorithm which can utilize edge information. In contrast with most existing algorithms, we do not simply use corner or line features. Rather, we incorporate edge (without making a straight line assumption) information with a smoothing term to enable computation of structure from motion from scenes which are dominated by strong edge information but lacking in corner features. Finally, in chapter 4, we use an algorithm similar to that in chapter 3, to enable the computation of inter-image mosaicing on image pairs with parallax, without the need to explicitly compute structure from motion.