Computer vision is concerned with the recovery of useful scene or camera information from a set of images. One classical problem is the estimation of the 3D scene structure depicted in multiple photographs. Such estimation fundamentally requires determining how the cameras are related in space. For a dynamic event recorded by multiple video cameras, finding the temporal relationship between cameras has a similar importance. Estimating such synchrony is key to a further analysis of the dynamic scene components. Existing approaches to synchronisation involve using visual cues common to both videos, and consider a discrete uniform range of synchronisation hypotheses. These prior methods exploit known constraints which hold in the presence of synchrony, from which both a temporal relationship, and an unchanging spatial relationship between the cameras can be recovered. This thesis presents methods that synchronise a pair of independently moving cameras. The spatial configuration of cameras is assumed to be known, and a cost function is developed to measure the quality of synchrony even for accuracies within a fraction of a frame. A Histogram method is developed which changes the approach from a consideration of multiple synchronisation hypotheses, to searching for seemingly synchronous frame pairs independently. Such a strategy has increased efficiency in the case of unknown frame rates. Further savings can be achieved by reducing the sampling rate of the search, by only testing for synchrony across a small subset of frames. Two robust algorithms are devised, using Bayesian inference to adaptively seek the sampling rate that minimises total execution time. These algorithms have a general underlying premise, and should be applicable to a wider class of robust estimation problems. A method is also devised to robustly synchronise two moving cameras when their spatial relationship is unknown. It is assumed that the motion of each camera has been estimated independently, so that these motion estimates are unregistered. The algorithm recovers both a synchronisation estimate, and a 3D transformation that spatially registers the two cameras. / Thesis (Ph.D.) - University of Adelaide, School of Computer Science, 2008