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Fundamental problems in computational video

Problems in computer vision and computational video often make certain assumptions about the input data. For example, structure from motion algorithms assume a baseline of minimal configuration and reconstruction problems often assume a known corresponding feature set and calibration parameters. Often it is the case that these assumptions present difficult problems in themselves. 
This thesis examines problems that maintain a common thread of camera geometries and feature tracking in video data. Often coined computational video, this emerging field presents a number of interesting problems that often fall into the assumptions of other research areas. Specifically, we address suitable baseline selection for structure from motion as well as an automated system that solves the correspondence problem in a large number of cases. We also address a means for automatically computing intrinsic camera parameters for long video sequences and we examine a method for synchronizing multiple video streams. Furthermore, we address the problem of accurate segmentation of commercial video streams for subsequent use in video databases and search facilities. 
Each of the problems addressed in this thesis are often assumed to be solved in the presentation of other research problems such as 3D reconstruction, video abstracting and database population. Each of the proposed solutions provides benefits to the research community by providing tools and/or novel algorithms that address these often assumed sub-problems. Furthermore, the findings presented in this thesis remove a number of constraints that are generally placed on these types of problems.