[Truncated abstract] Video sequence synchronization is necessary for any computer vision application that integrates data from multiple simultaneously recorded video sequences. With the increased availability of video cameras as either dedicated devices, or as components within digital cameras or mobile phones, a large volume of video data is available as input for a growing range of computer vision applications that process multiple video sequences. To ensure that the output of these applications is correct, accurate video sequence synchronization is essential. Whilst hardware synchronization methods can embed timestamps into each sequence on-the-fly, they require specialized hardware and it is necessary to set up the camera network in advance. On the other hand, computer vision-based software synchronization algorithms can be used to post-process video sequences recorded by cameras that are not networked, such as common consumer hand-held video cameras or cameras embedded in mobile phones, or to synchronize historical videos for which hardware synchronization was not possible. The current state-of-the-art software algorithms vary in their input and output requirements and camera configuration assumptions. ... Next, I describe an approach that synchronizes two video sequences where an object exhibits ballistic motions. Given the epipolar geometry relating the two cameras and the imaged ballistic trajectory of an object, the algorithm uses a novel iterative approach that exploits object motion to rapidly determine pairs of temporally corresponding frames. This algorithm accurately synchronizes videos recorded at different frame rates and takes few iterations to converge to sub-frame accuracy. Whereas the method presented by the first algorithm integrates tracking data from all frames to synchronize the sequences as a whole, this algorithm recovers the synchronization by locating pairs of temporally corresponding frames in each sequence. Finally, I introduce an algorithm for synchronizing two video sequences recorded by stationary cameras with unknown epipolar geometry. This approach is unique in that it recovers both the frame rate ratio and the frame offset of the two sequences by finding matching space-time interest points that represent events in each sequence; the algorithm does not require object tracking. RANSAC-based approaches that take a set of putatively matching interest points and recover either a homography or a fundamental matrix relating a pair of still images are well known. This algorithm extends these techniques using space-time interest points in place of spatial features, and uses nested instances of RANSAC to also recover the frame rate ratio and frame offset of a pair of video sequences. In this thesis, it is demonstrated that each of the above algorithms can accurately recover the frame rate ratio and frame offset of a range of real video sequences. Each algorithm makes a contribution to the body of video sequence synchronization literature, and it is shown that the synchronization problem can be solved using a range of approaches.

<p>Sustained attention, the ability to direct and maintain attentional focus on tasks and stimuli, emerges during infancy and undergoes rapid development throughout early childhood. Abnormal patterns of sustained attention are implicated in several childhood psychological disorders. Improving our measurement of infant sustained attention may clarify how child psychopathology develops and inform targeted prevention and early intervention efforts. While several behavioral and psychophysiological measures index infant sustained attention, previous studies have employed these measures in isolation, focused on analyses at short timescales of milliseconds to a few seconds, and examined synchronous associations among these measures. Therefore, the associations and temporal relationships across multiple, concurrent behavioral and psychophysiological measures of infant sustained attention remain unclear, particularly at long timescales. The present study assessed sustained attention in 12-month-old infants using behavioral (looking), cardiac (heart rate), and neural (theta and alpha oscillations) measures to investigate two temporal aspects of infant sustained attention. First, we examined whether associations among infant sustained attention measures were similar or different across short (1-second) and long (10-second) timescales. Covariation analyses indicated largely similar association patterns among these measures across the two timescales. Second, we evaluated whether specific infant sustained attention measures temporally preceded other measures. Cross-correlation analyses broadly revealed that short-timescale measures exhibited asynchronous temporal relationships, such that looking behaviors preceded neural oscillations that in turn preceded cardiac responses. Our findings highlight the value of considering the temporal dimension when studying and measuring infant sustained attention. Additional multimodal research may yield greater insights into dynamic biobehavioral processes that underlie infant sustained attention and enhance clinical interventions aimed at promoting optimal outcomes for young children with abnormalities in sustained attention.</p>