Panos Partsinevelos

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Spatiotemporal helixes are a novel way to model spatiotemporal change. They represent both the movement of an object, as it is expressed by the trajectory of its center, and the changes of its outline. Accordingly they are highly suitable to communicate the evolution of phenomena as they are captured e.g. in sequences of imagery. In this paper we present(More)
Motion imagery datasets capture evolving phenomena like the movement of a car or the progress of a natural disaster at video or quasi-video rates. The identification of individual spatiotemporal trajectories from such datasets is farm from trivial when these trajectories intersect in space, time, or attributes. In this paper we present our approach to this(More)
In this paper we address the problem of analyzing and managing complex dynamic scenes captured in video. We present an approach to summarize video datasets by analyzing the trajectories of objects within them. Our work is based on the identification of nodes in these trajectories as critical points that describe the behavior of an object over a video(More)
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