Preface

摘要

Most of the leading algorithms in computer vision are based on global optimization methods. Such methods compute the solution of a given problem as minimizer of a suitable cost functional that penalizes deviations from previously made assumptions and integrates them in a global manner, i.e., over the entire image domain. Since this way of modelling is very transparent and intuitive, it is not surprising that such methods have become popular and successful tools to tackle many fundamental problems in computer vision such as, e.g., motion estimation, stereo reconstruction, image restoration, and object segmentation. However, there is also a price to pay when employing global optimization methods. The corresponding cost functionals often lead to optimization problems that are both mathematically challenging and computationally expensive.