Browsing by Author "Ganglberger, Florian"

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    Feasibility Study For Automatic Bird Tracking and Visualization from Time-Dependent Marine Radar Imagery
    (The Eurographics Association, 2019) Ganglberger, Florian; Bühler, Katja; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
    In recent years, radar technology has increasingly been used for the monitoring of bird migration. Marine radars are often utilized for this purpose because of their wide accessibility, range, and resolution. They allow the tracking of birds even at night-when most bird migration takes place-over extended periods of time. This creates a wealth of radar images, for which manual annotation of bird tracks is not feasible. We propose a tool for automatic bird tracking and visualization from marine radar imagery. For this purpose, we developed a bird tracking algorithm for vertically recorded radar images that is able to extract quantitative parameters including flight direction, height, and duration. The results can be qualitatively verified by a visualization design that enables domain experts the time-dependent visualization of bird tracks. Furthermore, it allows a preprocessing of radar images taken by screen capturing for device independence. Our tool was used in an ornithological monitoring study to analyze over 200.000 vertically recorded radar images taken in multiple observation periods and locations.
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    Visualising the Transition of Large Networks via Dimensionality Reduction to Illustrate the Evolution of the Human Brain
    (The Eurographics Association, 2021) Ganglberger, Florian; Kaczanowska, Joanna; Haubensak, Wulf; Bühler, Katja; Theisel, Holger and Wimmer, Michael
    Advances in high-throughput imaging techniques enable the creation of networks depicting spatio-temporal biological and neurophysiological processes with unprecedented size and magnitude. These networks involve thousands of nodes, which can not be compared over time by traditional methods due to complexity and clutter. When investigating networks over multiple time steps, a crucial question for the visualisation research community becomes apparent: How to visually trace changes of the connectivity over several transitions? Therefore, we developed an easy-to-use method that maps multiple networks to a common embedding space. Visualising the distribution of node-clusters of interest (e.g. brain regions) enables their tracing over time. We demonstrate this approach by visualizing spatial co-evolution networks of different evolutionary timepoints as small multiples to investigate how the human brain genetically and functionally evolved over the mammalian lineage.