DSpace Repository :: Browsing by Author "Ynnerman, Anders"

Browsing by Author "Ynnerman, Anders"

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Design of a Real Time Visual Analytics Support Tool for Conflict Detection and Resolution in Air Traffic Control
(The Eurographics Association, 2020) Zohrevandi, Elmira; Westin, Carl A. L.; Lundberg, Jonas; Ynnerman, Anders; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
Air traffic control is a safety critical high-risk environment where operators need to analyse and interpret traffic dynamics of spatio-temporal data in real-time. To support the air traffic controller in safely separating traffic, earlier research has applied real-time visualisation techniques that explore the constraints and solution spaces of separation problems. Traditionally, situation displays for conflict detection and resolution have used visualisations that convey information about the relative horizontal position between aircraft. Although vertical solutions for solving conflicts are common, and often a preferred among controllers, visualisations typically provide limited information about the vertical relationship between aircraft. This paper presents a design study of an interactive conflict detection and resolution support tool and explores techniques for real-time visualisation of spatio-temporal data. The design evolution has incorporated several activities, including an initial work domain analysis, iterative rounds of programming, design, and evaluations with a domain expert, and an evaluation with eight active controllers. The heading-time-altitude visualisation system is developed based on formulating and solving aircraft movements in a relative coordinate system. A polar-graph visualisation technique is used to construct a view of conflicting aircraft vertical solution spaces in the temporal domain. Using composite glyphs, the final heading-time-altitude visualisation provides a graphical representation of both horizontal and vertical solution spaces for the traffic situation.
Feature Exploration using Local Frequency Distributions in Computed Tomography Data
(The Eurographics Association, 2020) Falk, Martin; Ljung, Patric; Lundström, Claes; Ynnerman, Anders; Hotz, Ingrid; Kozlíková, Barbora and Krone, Michael and Smit, Noeska and Nieselt, Kay and Raidou, Renata Georgia
Frequency distributions (FD) are an important instrument when analyzing and investigating scientific data. In volumetric visualization, for example, frequency distributions visualized as histograms, often assist the user in the process of designing transfer function (TF) primitives. Yet a single point in the distribution can correspond to multiple features in the data, particularly in low-dimensional TFs that dominate time-critical domains such as health care. In this paper, we propose contributions to the area of medical volume data exploration, in particular Computed Tomography (CT) data, based on the decomposition of local frequency distributions (LFD). By considering the local neighborhood utilizing LFDs we can incorporate a measure for neighborhood similarity to differentiate features thereby enhancing the classification abilities of existing methods. This also allows us to link the attribute space of the histogram with the spatial properties of the data to improve the user experience and simplify the exploration step. We propose three approaches for data exploration which we illustrate with several visualization cases highlighting distinct features that are not identifiable when considering only the global frequency distribution. We demonstrate the power of the method on selected datasets.
MolFind - Integrated Multi-Selection Schemes for Complex Molecular Structures
(The Eurographics Association, 2019) Skånberg, Robin; Linares, Mathieu; Falk, Martin; Hotz, Ingrid; Ynnerman, Anders; Byska, Jan and Krone, Michael and Sommer, Björn
Selecting components and observing changes of properties and configurations over time is an important step in the analysis of molecular dynamics (MD) data. In this paper, we present a selection tool combining text-based queries with spatial selection and filtering. Morphological operations facilitate refinement of the selection by growth operators, e.g. across covalent bonds. The combination of different selection paradigms enables flexible and intuitive analysis on different levels of detail and visual depiction of molecular events. Immediate visual feedback during interactions ensures a smooth exploration of the data. We demonstrate the utility of our selection framework by analyzing temporal changes in the secondary structure of poly-alanine and the binding of aspirin to phospholipase A2.
The State of the Art of Spatial Interfaces for 3D Visualization
(© 2021 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2021) Besançon, Lonni; Ynnerman, Anders; Keefe, Daniel F.; Yu, Lingyun; Isenberg, Tobias; Benes, Bedrich and Hauser, Helwig
We survey the state of the art of spatial interfaces for 3D visualization. Interaction techniques are crucial to data visualization processes and the visualization research community has been calling for more research on interaction for years. Yet, research papers focusing on interaction techniques, in particular for 3D visualization purposes, are not always published in visualization venues, sometimes making it challenging to synthesize the latest interaction and visualization results. We therefore introduce a taxonomy of interaction technique for 3D visualization. The taxonomy is organized along two axes: the primary source of input on the one hand and the visualization task they support on the other hand. Surveying the state of the art allows us to highlight specific challenges and missed opportunities for research in 3D visualization. In particular, we call for additional research in: (1) controlling 3D visualization widgets to help scientists better understand their data, (2) 3D interaction techniques for dissemination, which are under‐explored yet show great promise for helping museum and science centers in their mission to share recent knowledge, and (3) developing new measures that move beyond traditional time and errors metrics for evaluating visualizations that include spatial interaction.
Visual Analysis of the Impact of Neural Network Hyper-Parameters
(The Eurographics Association, 2020) Jönsson, Daniel; Eilertsen, Gabriel; Shi, Hezi; Zheng, Jianmin; Ynnerman, Anders; Unger, Jonas; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
We present an analysis of the impact of hyper-parameters for an ensemble of neural networks using tailored visualization techniques to understand the complicated relationship between hyper-parameters and model performance. The high-dimensional error surface spanned by the wide range of hyper-parameters used to specify and optimize neural networks is difficult to characterize - it is non-convex and discontinuous, and there could be complex local dependencies between hyper-parameters. To explore these dependencies, we make use of a large number of sampled relations between hyper-parameters and end performance, retrieved from thousands of individually trained convolutional neural network classifiers. We use a structured selection of visualization techniques to analyze the impact of different combinations of hyper-parameters. The results reveal how complicated dependencies between hyper-parameters influence the end performance, demonstrating how the complete picture painted by considering a large number of trainings simultaneously can aid in understanding the impact of hyper-parameter combinations.
A Visual Environment for Hypothesis Formation and Reasoning in Studies with fMRI and Multivariate Clinical Data
(The Eurographics Association, 2019) Jönsson, Daniel; Bergström, Albin; Forsell, Camilla; Simon, Rozalyn; Engström, Maria; Ynnerman, Anders; Hotz, Ingrid; Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia
We present an interactive visual environment for linked analysis of brain imaging and clinical measurements. The environment is developed in an iterative participatory design process involving neuroscientists investigating the causes of brain-related complex diseases. The hypotheses formation process about correlations between active brain regions and physiological or psychological factors in studies with hundreds of subjects is a central part of the investigation. Observing the reasoning patterns during hypotheses formation, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration, thus limiting the scientific scope and preventing extraction of knowledge from available data. Based on these observations, we designed methods that support neuroscientists by integrating their existing statistical analysis of multivariate subject data with interactive visual exploration to enable them to better understand differences between patient groups and the complex bidirectional interplay between clinical measurement and the brain. These exploration concepts enable neuroscientists, for the first time during their investigations, to interactively move between and reason about questions such as 'which clinical measurements are correlated with a specific brain region?' or 'are there differences in brain activity between depressed young and old subjects?'. The environment uses parallel coordinates for effective overview and selection of subject groups, Welch's t-test to filter out brain regions with statistically significant differences, and multiple visualizations of Pearson correlations between brain regions and clinical parameters to facilitate correlation analysis. A qualitative user study was performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights, and is an effective support tool for hypothesis formation.
Visualization Challenges of Variant Interpretation in Multiscale NGS Data
(The Eurographics Association, 2022) Ståhlbom, Emilia; Molin, Jesper; Lundström, Claes; Ynnerman, Anders; Krone, Michael; Lenti, Simone; Schmidt, Johanna
There is currently a movement in health care towards precision medicine, where genomics often is the central diagnostic component for tailoring the treatment to the individual patient. We here present results from a domain characterization effort to pinpoint problems and possibilities for visualization of genomics data in the clinical workflow, with analysis of copy number variants as an example task. Five distinct characteristics have been identified. Clinical genomics data is inherently multiscale, riddled with artifacts and uncertainty, and many findings have unknown significance, so it is a challenging visual analytics domain. Moreover, as in other clinical domains, high efficiency is key. This characterization will form the basis for follow-on visualization prototyping.
Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Lan, Fangfei; Young, Michael; Anderson, Lauren; Ynnerman, Anders; Bock, Alexander; Borkin, Michelle A.; Forbes, Angus G.; Kollmeier, Juna A.; Wang, Bei; Smit, Noeska and Vrotsou, Katerina and Wang, Bei
We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are classified based on five categories: data wrangling, data exploration, feature identification, object reconstruction, as well as education and outreach. Our unique contribution is to combine the diverse viewpoints from both astronomers and visualization experts to identify challenges and opportunities for visualization in astrophysics. The main goal is to provide a reference point to bring modern data analysis and visualization techniques to the rich datasets in astrophysics.
VisualNeuro: A Hypothesis Formation and Reasoning Application for Multi‐Variate Brain Cohort Study Data
(© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Jönsson, Daniel; Bergström, Albin; Forsell, Camilla; Simon, Rozalyn; Engström, Maria; Walter, Susanna; Ynnerman, Anders; Hotz, Ingrid; Benes, Bedrich and Hauser, Helwig
We present an application, and its development process, for interactive visual analysis of brain imaging data and clinical measurements. The application targets neuroscientists interested in understanding the correlations between active brain regions and physiological or psychological factors. The application has been developed in a participatory design process and has subsequently been released as the free software ‘VisualNeuro’. From initial observations of the neuroscientists' workflow, we concluded that while existing tools provide powerful analysis options, they lack effective interactive exploration requiring the use of many tools side by side. Consequently, our application has been designed to simplify the workflow combining statistical analysis with interactive visual exploration. The resulting environment comprises parallel coordinates for effective overview and selection, Welch's t‐test to filter out brain regions with statistically significant differences and multiple visualizations for comparison between brain regions and clinical parameters. These exploration concepts enable neuroscientists to interactively explore the complex bidirectional interplay between clinical and brain measurements and easily compare different patient groups. A qualitative user study has been performed with three neuroscientists from different domains. The study shows that the developed environment supports simultaneous analysis of more parameters, provides rapid pathways to insights and is an effective tool for hypothesis formation.

Browsing by Author "Ynnerman, Anders"

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