Convex Relaxation Methods for Image Segmentation and Stereo Reconstruction

We present advances in several fundamental fields of computer vision: image segmentation, object tracking, stereo reconstruction for depth map estimation and full 3D multi-view reconstruction. The basic method applied to these fields is convex relaxation. Convex relaxation methods allow for global optimization of numerous energy functionals and provide a step towards less user input and more automation. We will show how the respective computer vision problems can be formulated in this convex optimization framework. Efficient parallel implementations of the arising numerical schemes using graphics processing units allow for interactive applications.