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In atmospheric sciences, sizes of data sets grow continuously due to increasing resolutions. A central task is the comparison of spa-tiotemporal fields, to assess different simulations and to compare simulations with observations. A significant information reduction is possible by focusing on geometric-topological features of the fields or on derived meteorological objects. Due to the huge size of...
Human action recognition requires the description of complex motion patterns in image sequences. In general, these patterns span varying temporal scales. In this context, Lagrangian methods have proven to be valuable for crowd analysis tasks such as crowd segmentation. In this paper, we show that, besides their potential in describing large scale motion patterns, Lagrangian methods are also well suited...
The availability of dense motion information in computer vision domain allows for the effective application of Lagrangian techniques that have their origin in fluid flow analysis and dynamical systems theory. A well established technique that has been proven to be useful in image-based crowd analysis are Finite Time Lyapunov Exponents (FTLE). Based on this, we present a method to detect people carrying...
The extraction of motion patterns from image sequences based on the optical flow methodology is an important and timely topic among visual multi media applications. In this work we will present a novel framework that combines the optical flow methodology from image processing with methods developed for the Lagrangian analysis of time-dependent vector fields. The Lagrangian approach has been proven...
The Finite Time Lyapunov Exponent (FTLE) has become a widespread tool for analyzing unsteady flow behavior. For its computation, several numerical methods have been introduced, which provide trade-offs between performance and accuracy. In order to decide which methods and parameter settings are suitable for a particular application, an evaluation of the different FTLE methods is necessary. We propose...
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