Abstract
This talk discusses a number of techniques for correspondence estimation between stereo image pairs, i.e. two images of the same scene taken from different positions. The problem is to identify pairs of pixels in the two images that are the projections of the same scene point. Although the human visual system performs this task with ease, developing algorithms for automatically computing correspondences is a challenging task. In particular, existing algorithms can fail in homogeneous areas, near depth discontinuities and occlusions or with a repetitive texture pattern.
The first part of this talk focuses on seed propagation-based approaches that are a special case of local methods based computing an iterative solution, where the solution is initialised using a sparse set of reliable matches (the seeds). I introduce a reliability measure used by the propagation technique for finding the correct correspondent of a pixel, providing robustness in the context of the above difficulties. This measure takes into account an unambiguity term, a continuity term and a colour consistency term. It has the advantage of taking into account information from the other candidates, and leads, according to our experimental evaluation, to better results when compared to other methods based on a correlation score alone.
In the second part of this talk I will present ongoing work in our group on stereo matching in urban environments. In particular we exploit the fact that images of such environments contain multiple planar elements. I will show how utilising this strong geometrical constraint allows us to automatically segment building facades in single images. Furthermore I show how this technique permits robust pixel matching in wide-baseline stereo pairs. Finally, I will discuss how we intend to apply this technique for the development of augmented reality applications.
