Browsing by Author "Kurzhals, Kuno"

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Open Access
Fast-forward video visualization
(2012) Kurzhals, Kuno
Durch den Einsatz von Überwachungskameras kann sich eine sehr große Menge an Videomaterial ansammeln. Wenn es nötig ist, dieses Material manuell zu betrachten, so kann man durch die Verwendung des Schnellvorlaufs die Betrachtungszeit verkürzen. Bei digitalen Videos können dabei hohe Beschleunigungen durch das Überspringen von Bildern erreicht werden. Diese Sprünge können jedoch zu „Change Blindness“ führen und den Beobachter wichtige Ereignisse verpassen lassen. Im Rahmen dieser Diplomarbeit werden deshalb Methoden vorgestellt, die auf verschiedene Weise versuchen, die Informationen der übersprungenen Bilder wieder sichtbar zu machen. Biologisch motiviertes Blending erzeugt eine Bewegungsunschärfe bei den Objekten. Die Differenzen-Methode erzeugt multiple Instanzen der Objekte im Verlauf ihrer Bewegung. Beim Tracking werden die Objekte mit Pfeilen und Schweifen versehen, die Informationen über vergangene und zukünftige Bewegungen der Objekte vermitteln. Da es in Überwachungsvideos häufig Situationen gibt, in denen nichts passiert, kann man durch Adaptive Fast-Forward unterschiedliche Beschleunigungen nach Priorität der Ereignisse verwenden. Drei verschiedene Geschwindigkeitsvisualisierungen werden vorgestellt, die den Wechsel zwischen den Beschleunigungen besser vermitteln sollen. In einer Benutzerstudie werden dann die Methoden für den Schnellvorlauf genauer untersucht. Dabei wird die Objekterkennung und die Verfolgung von Bewegungen geprüft. Die Geschwindigkeitsvisualisierungen werden in einer zusätzlichen Aufgabe hinsichtlich ihrer Effektivität und Beanspruchung miteinander verglichen.
Open Access
Visual analysis of fitness landscapes in architectural design optimization
(2024) Abdelaal, Moataz; Galuschka, Marcel; Zorn, Max; Kannenberg, Fabian; Menges, Achim; Wortmann, Thomas; Weiskopf, Daniel; Kurzhals, Kuno
In architectural design optimization, fitness landscapes are used to visualize design space parameters in relation to one or more objective functions for which they are being optimized. In our design study with domain experts, we developed a visual analytics framework for exploring and analyzing fitness landscapes spanning data, projection, and visualization layers. Within the data layer, we employ two surrogate models and three sampling strategies to efficiently generate a wide array of landscapes. On the projection layer, we use star coordinates and UMAP as two alternative methods for obtaining a 2D embedding of the design space. Our interactive user interface can visualize fitness landscapes as a continuous density map or a discrete glyph-based map. We investigate the influence of surrogate models and sampling strategies on the resulting fitness landscapes in a parameter study. Additionally, we present findings from a user study ( N = 12), revealing how experts’ preferences regarding projection methods and visual representations may be influenced by their level of expertise, characteristics of the techniques, and the specific task at hand. Furthermore, we demonstrate the usability and usefulness of our framework by a case study from the architecture domain, involving one domain expert.
Open Access
Visual analytics of eye-tracking and video data
(2018) Kurzhals, Kuno; Weiskopf, Daniel (Prof. Dr.)
Eye tracking, i.e., the detection of gaze points, becomes increasingly popular in numerous research areas as a means to investigate perceptual and cognitive processes. In comparison to other evaluation methods, eye tracking provides insights into the distribution of attention and sequential viewing behavior, which are essential for many research questions. For visualization research, such insights help assess a visualization design and identify potential flaws. Gaze data coupled with a visual stimulus poses a complex analysis problem that is approached by statistical and visual methods. Statistical methods are often limited to hypothesis-driven evaluation and modeling of processes. Visualization is applied to confirm statistical results and for exploratory data analysis to form new hypotheses. Surveying the state of the art of visualizations for eye tracking shows a deficiency of appropriate methods, particularly for dynamic stimuli (e.g., videos). Video visualization and visual analytics provide methods that can be adapted to perform the required analysis processes. The automatic processing of video and gaze data is combined with interactive visualizations to provide an overview of the data, support efficient browsing, detect interesting events, and annotate important parts of the data. The techniques developed for this thesis focus on the analysis of videos from remote and from mobile eye tracking. The discussed remote eye-tracking scenarios consist of one video that is investigated by multiple participants. Mobile eye tracking comprises scenarios in which participants wear glasses with a built-in device to record their gaze. Both types of scenarios pose individual challenges that have to be addressed for an effective analysis. In general, the comparison of gaze behavior between participants plays an important role to detect common behavior and outliers. This thesis addresses the topic of eye tracking and visualization bidirectionally: Eye tracking is applied in user studies to evaluate visualization techniques beyond established performance measures and questionnaires. The current application of eye tracking in visualization research is surveyed. Further, it is discussed how existing methodology can be extended to incorporate eye tracking for future analysis scenarios. Vice versa, a set of new visualization techniques for data from remote and mobile eye-tracking devices are introduced that support the analysis of gaze behavior in general. Here, techniques for raw data and for data with annotations are introduced, as well as approaches to perform the tedious annotation process more efficiently.