Low Rank Matrix Factorization and Relative Pose Problems in Computer Vision
Popular Abstract in English The ultimate goal of computer vision is to make computers "see" like humans do. Toward this goal, one essential step is to enable computers to perceive a three-dimensional (3D) space as in the real world. In this thesis, we investigate the problem of reconstructing a 3D scene model from ordinary two-dimensional (2D) images. More specifically, given a set of images of thThis thesis is focused on geometric computer vision problems. The first part of the thesis aims at solving one fundamental problem, namely low-rank matrix factorization. We provide several novel insights into the problem. In brief, we characterize, generate, parametrize and solve the minimal problems associated with low-rank matrix factorization. Beyond that, we give several new algorithms based o