Discrete optimization methods and their applications in computer vision

简单地说，计算机视觉是教计算机看，也就是感知场景中存在哪些对象，它们位于何处以及它们正在移动。自动处理视觉信息的能力导致了几乎无限数量的应用，从传统的工业检查，安全和监控，机器人导航到较新的视频会议，虚拟旅行和电影业的特效。每年都有新的应用被开发出来。基于优化的计算机视觉方法特别有前途，并且已经证明在不同视觉领域的适用性方面非常成功。近年来，在诸如立体对应、图像分割、图像恢复、多视图重建等基本计算机视觉任务中取得了巨大的进展，这在很大程度上是由于有效的优化技术的发展。应该注意的是，最近的进展是由于优化算法本身，因为它们所应用的大多数目标函数已经在计算机视觉中使用了很长一段时间，但没有很好的方法来优化它们，因此，性能平平。我以前的研究大部分致力于开发高效的优化算法，专门为计算机视觉问题设计。同时，我对优化方法在特定计算机视觉任务中的应用非常感兴趣。我发现理论方法和实际应用之间存在协同作用。它是从试图解决一个实际的应用，人们发现有趣的新的优化问题。反过来，解决一个新的优化问题会导致实际应用的性能提高。我提出了两个研究工作，一个是针对优化技术的理论问题，另一个方向是对优化技术的实际应用。 这两个方向的研究是紧密交织在一起的。

Computer vision, simply stated, is about teaching computers to see, that is to perceive which objects are present in a scene, where they are located, and where they are moving. The ability to atomatically process visual information leads to a virtually unlimited number of applications, ranging from the traditional ones such as industrial inspection, security and surveillance, robot navigation, to the newer ones such as video conferencing, virtual travel, and special effects for the movie industry. New applications are being developed each year.An optimisation based approach to computer vision is particularly promising and has already proved quite successful in applicability across different vision domains. There has been a tremendous progress in recent years in such fundamental computer vision tasks as stereo correspondence, image segmentation, image restoration, multi-view reconstruction, in large part due to the development of effective optimisation techniques. It should be noted that the recent progress is due to the optimisation algorithms themselves, since most of the objective functions they are being applied to have been used in computer vision for a long time but without good means to optimise them, and, therefore, with mediocre performance. A large portion of my previous research has been devoted to developing efficient optimisation algorithms, designed specifically for computer vision problems. At the same time, I am very interested in applications of optimisation methods to specific computer vision tasks. I find that there is a synergy between the theoretical methods and practical applications. It is from trying to solve a practical application that one finds interesting new optimisation problems. In turn, solving a new optimisation problem leads to an improved  performance on a practical application. I propose two research efforts, one directed at the theoretical issues in optimisation based techniques, and the other direction is towards practical applications of optimisation techniques.  The research in these two directions is closely intertwined.