05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Institute: search.filters.UBSInstitut.Institut für Visualisierung und Interaktive SystemeInstitute: search.filters.UBSInstitut.Visualisierungsinstitut der Universität Stuttgart

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Now showing 1 - 5 of 5
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    Impact of gaze uncertainty on AOIs in information visualisations
    (2022) Wang, Yao; Koch, Maurice; Bâce, Mihai; Weiskopf, Daniel; Bulling, Andreas
    Gaze-based analysis of areas of interest (AOIs) is widely used in information visualisation research to understand how people explore visualisations or assess the quality of visualisations concerning key characteristics such as memorability. However, nearby AOIs in visualisations amplify the uncertainty caused by the gaze estimation error, which strongly influences the mapping between gaze samples or fixations and different AOIs. We contribute a novel investigation into gaze uncertainty and quantify its impact on AOI-based analysis on visualisations using two novel metrics: the Flipping Candidate Rate (FCR) and Hit Any AOI Rate (HAAR). Our analysis of 40 real-world visualisations, including human gaze and AOI annotations, shows that gaze uncertainty frequently and significantly impacts the analysis conducted in AOI-based studies. Moreover, we analysed four visualisation types and found that bar and scatter plots are usually designed in a way that causes more uncertainty than line and pie plots in gaze-based analysis.
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    Group diagrams for simplified representation of scanpaths
    (2023) Schäfer, Peter; Rodrigues, Nils; Weiskopf, Daniel; Storandt, Sabine
    We instrument Group Diagrams (GDs) to reduce clutter in sets of eye-tracking scanpaths. Group Diagrams consist of trajectory subsets that cover, or represent, the whole set of trajectories with respect to some distance measure and an adjustable distance threshold. The original GDs allow for an application of various distance measures. We implement the GD framework and evaluate it on scanpaths that were collected by a former user study on public transit maps. We find that the Fréchet distance is the most appropriate measure to get meaningful results, yet it is flexible enough to cover outliers. We discuss several implementation-specific challenges and improve the scalability of the algorithm. To evaluate our results, we conducted a qualitative study with a group of eye-tracking experts. Finally, we note that our enhancements are also beneficial within the original problem setting, suggesting that our approach might be applicable to various types of input data.
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    Efficient and robust background modeling with dynamic mode decomposition
    (2022) Krake, Tim; Bruhn, Andrés; Eberhardt, Bernhard; Weiskopf, Daniel
    A large number of modern video background modeling algorithms deal with computational costly minimization problems that often need parameter adjustments. While in most cases spatial and temporal constraints are added artificially to the minimization process, our approach is to exploit Dynamic Mode Decomposition (DMD), a spectral decomposition technique that naturally extracts spatio-temporal patterns from data. Applied to video data, DMD can compute background models. However, the original DMD algorithm for background modeling is neither efficient nor robust. In this paper, we present an equivalent reformulation with constraints leading to a more suitable decomposition into fore- and background. Due to the reformulation, which uses sparse and low-dimensional structures, an efficient and robust algorithm is derived that computes accurate background models. Moreover, we show how our approach can be extended to RGB data, data with periodic parts, and streaming data enabling a versatile use.
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    Immersive analysis of multi-scalar field point clouds
    (2025) Flach, Ayla-Irina
    Point cloud data is increasingly used as a digital representation of three-dimensional objects in the real world. As acquisition devices become more commonly available (some smartphones now include Light Detection and Ranging (LiDAR) sensors), “intelligent” buildings provide growing amounts of multi-variate data and the size of the resulting point clouds continues to increase, novel techniques for visualization and exploration of the data within its spatial context are required. Traditional tools for this purpose rely on two-dimensional desktop environments which often pose challenges such as a steep learning curve and difficulties in correctly conveying spatial context. Recent research has explored the use of Virtual Reality (VR) for a more immersive exploration of point clouds. This project introduces an immersive VR environment, which provides the ability to explore multiple scalar fields associated with point cloud data using two distinct visualization methods. Additionally, users can annotate the point cloud with a virtual painting device while navigating with natural walking movement by means of an omnidirectional treadmill. This functionality can be used for manual classification of objects in the point cloud as well as for generation of artificial scalar data where none is available. A pilot study is then conducted to assess user satisfaction and system usability.
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    Interaktive NMF-basierte visuelle Analyse für Zeitreihen-Ensembles
    (2025) Konrad, David
    Heutzutage werden in vielen Bereichen Zeitreihen-Ensembles erzeugt, die häufig rein numerische Werte enthalten. Dabei verbergen sich in diesen Daten oft wertvolle Informationen, die durch bloßes Betrachten nur schwer zu erkennen sind. Daher widmen sich zahlreiche wissenschaftliche Arbeiten der Entwicklung und Verbesserung von Methoden zur Analyse solcher Datensätze. Häufig werden Machine-Learning-Ansätze genutzt, um zugrundeliegende Muster aufzudecken, die allerdings oftmals als Blackbox fungieren und wenig nachvollziehbar sind. Algorithmen, welche die Daten als Matrix faktorisieren, liefern hingegen eine nachvollziehbare Zerlegung. Sind die Datenwerte nichtnegativ, bietet sich besonders Nichtnegative Matrixfaktorisierung (NMF) für das Formen einer Faktorisierung an. NMF bildet Komponenten, die zeitliche Strukturen und Gruppierungen der Zeitreihen aufzeigen. Da diese ebenfalls nichtnegativ sind, lassen sich die Ergebnisse hierbei besonders gut visuell interpretieren und vergleichen. Mit interaktiven Methoden zur Visualisierung kann diese Eigenschaft von NMF-Zerlegungen noch besser genutzt werden. In dieser Arbeit stellen wir ein neue, Web-basierte Anwendung vor, mit der sich nicht negative, numerische Zeitreihen-Ensembles analysieren lassen. Dabei liegt der Fokus auf einem „Human-inthe-Loop“-fokussierten Konzept. Anhand von interaktiven Visualisierungen wird eine explorative Vorgehensweise ermöglicht. Dadurch kann der Nutzer den Analyseprozess mitformen und bei Bedarf Eingabeparameter für weitere Analysen anpassen. Hierzu werden zunächst Grundlagen des Themenbereichs eingeführt. Die zur Analyse der NMF-Komponenten notwendigen Methoden werden vorgestellt und im Tool implementiert. Darunter befinden sich insbesondere Methoden zur Ranganalyse für NMF. Anschließend wird der Aufbau des Tools detailliert beschrieben, welcher sich an den Grundsätzen der Visual Analytics (VA) orientiert. In zwei Fallstudien wird anhand von Zeitreihe-Ensemble-Datensätzen demonstriert, wie die App verwendet werden kann, um diese explorativ zu untersuchen.