EuroVisPosters2022

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https://diglib.eg.org/handle/10.2312/2633198

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Accurate Molecular Atom Selection in VR
(The Eurographics Association, 2022) Molina, Elena; Vázquez, Pere-Pau; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Accurate selection in cluttered scenes is complex because a high amount of precision is required. In Virtual Reality Environments, it is even worse, because it is more difficult for us to point a small object with our arms in the air. Not only our arms move slightly, but the button/trigger press reduces our weak stability. In this paper, we present two alternatives to the classical ray pointing intended to facilitate the selection of atoms in molecular environments. We have implemented and analyzed such techniques through an informal user study and found that they were highly appreciated by the users. This selection method could be interesting in other crowded environments beyond molecular visualization.
ANARI: ANAlytic Rendering Interface
(The Eurographics Association, 2022) Griffin, Kevin; Amstutz, Jefferson; DeMarle, Dave; Günther, Johannes; Progsch, Jakob; Sherman, William; Stone, John E.; Usher, Will; Kooten, Kees van; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The ANARI API enables users to build the description of a scene to generate imagery, rather than specifying the details of the rendering process, providing simplified visualization application development and cross-vendor portability to diverse rendering engines, including those using state-of-the-art ray tracing.
Automatic Segmentation of Tooth Images: Optimization of Multi-parameter Image Processing Workflow
(The Eurographics Association, 2022) Bressan Fogalli, Giovani; Line, Sérgio Roberto Peres; Baum, Daniel; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The development of specific algorithms in image processing are usually related to dataset characteristics. Those characteristics will influence the number of instructions required to solve a problem. Normally, the more complex a set of instructions is, the more parameters need to be set. Dealing with such degrees of freedom, sometimes leading to subjective decision making, is time-consuming and frequently leads to errors or sub-optimal results of the developed model. Here, we deal with a model for segmentation of masks of tooth images containing a pattern of bands called Hunter-Schreger Bands (HSB). They appear on tooth surface when lit from the side. This segmentation process is only one step of a pipeline whose overall goal is human biometric identification to be used, e.g., in forensics. The segmentation algorithm, which exploits the anisotropy of the image, uses several parameters and choosing the optimal combination of them is challenging. The aim of this work was to utilize visual data analysis tools to optimize the chosen parameters and to understand their influence on the performance of the algorithm. Our results reveal that a slightly better combination of parameter values can be found starting from the experimentally determined initial parameters. This approach can be repeatedly performed to achieve even better parameterizations. To more deeply understand the influence of the parameters on the final result, more sophisticated visual interaction tools will be explored in future work.
A Case Study on Implementing Screen Reader Accessibility in Dynamic Visualizations
(The Eurographics Association, 2022) Costa, Rita; Malveiro, Beatriz; Palmeiro, João; Bizarro, Pedro; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Millions of people worldwide work in jobs where assessing dynamic data presented visually to them is a key part of their tasks. Since the data is only represented in a visual format, these occupations are out of reach for visually impaired people, making them unable to review hundreds of information-heavy cases per day and determine outcomes for each one in just a couple of minutes. In this work, we aim to shrink that gap by detailing the implementation of screen reader accessibility features to real-world visualizations used by fraud detection analysts. We propose a set of features that should be validated with users and, if proved to be useful, transformed into guidelines for creating these types of accessible charts.
Chord2DS: An Extension to Chord Diagram to Show Data Elements from Two Heterogeneous Data Sources
(The Eurographics Association, 2022) Humayoun, Shah Rukh; Brahmadevara, Likhitha; Krone, Michael; Lenti, Simone; Schmidt, Johanna
The standard Chord diagram, a radial layout, shows data elements in a circular fashion from one data source. In this paper, we propose an extension to the standard Chord diagram to show data elements from two heterogeneous data sources into one single diagram. The main Chord diagram is used for showing data elements and the relations between them from one data source, while we use an outer layer to show data elements from the second data source. The relationships between data elements from both data sources are shown through visual cues. The proposed solution uses space efficiently compared to using multiple diagrams in the scenarios of two heterogeneous data sources.
Context Specific Visualizations on Smartwatches
(The Eurographics Association, 2022) Islam, Alaul; Blascheck, Tanja; Isenberg, Petra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present an analysis of the results of a full-day context-specific ideation exercise for smartwatch visualizations. Participants of the exercise created 34 sketches during a sightseeing activity. Our analysis of these sketches showed where visualizations could be applied and shown, what information needs they could target, and how data could be represented in the sightseeing context.
A Design Space for Explainable Ranking and Ranking Models
(The Eurographics Association, 2022) Hazwan, Ibrahim Al; Schmid, Jenny; Sachdeva, Madhav; Bernard, Jürgen; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Item ranking systems support users in multi-criteria decision-making tasks. Users need to trust rankings and ranking algorithms to reflect user preferences nicely while avoiding systematic errors and biases. However, today only few approaches help end users, model developers, and analysts to explain rankings. We report on the study of explanation approaches from the perspectives of recommender systems, explainable AI, and visualization research and propose the first cross-domain design space for explainers of item rankings. In addition, we leverage the descriptive power of the design space to characterize a) existing explainers and b) three main user groups involved in ranking explanation tasks. The generative power of the design space is a means for future designers and developers to create more target-oriented solutions in this only weakly exploited space.
Digital Twins of Smart Farms
(The Eurographics Association, 2022) Zhao, Yuhang; Jiang, Zheyu; Pang, Shanchen; Lv, Zhihan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
In recent years, the development of Digital Twins has made rapid progress, and Digital Twins has gradually begun to combine various fields and applied to the current digitalization of the physical world. Digital Twins can play an important role in agriculture. Digital Twins can fully improve the yield and income of crop products and solve the problems of food security. In this paper, the development prospect of Digital Twins in agriculture is discussed.
Enhancing Evaluation of Room Scale VR Studies to POI Visualizations in Minimaps
(The Eurographics Association, 2022) Ajdadilish, Batoul; Kohl, Steffi; Schröder, Kay; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Understanding and evaluating user behavior in virtual reality environments is challenging for researchers. Stereoscopic perception is highly dependent on the point of view, so it is necessary to account for multiple spatial positions. Robust tools and methods to analyze these spatio-temporal data are lacking. We propose a design solution for spatio-temporal data visualization for room-scale VR studies. Our result is a top-down minimap that plots 3D point of interest coordinates of room-scale virtual reality environments to a 2D visualization. The video stream from the head mount display is next to the minimap showing the top-down view of the scene, reflecting the visual stimuli that were perceivable by the user. Both views are linked such that replaying the user session is synchronized in time. The minimap enables researchers to review and replay the recorded user session for in-depth study, allowing them to gain insightful information about users' behavior in virtual environments.
EuroVis 2022 Posters: Frontmatter
(The Eurographics Association, 2022) Krone, Michael; Lenti, Simone; Schmidt, Johanna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Exploration and Analysis of Image-base Simulation Ensembles
(The Eurographics Association, 2022) Dahshan, Mai; Turton, Terece L.; Polys, Nicholas; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Scientists run simulation ensembles to study the behavior of a phenomenon using varying initial conditions or input parameters. However, the I/O bottlenecks hinder performing large-scale multidimensional simulations. In situ visualization approaches address the variability of I/O performance by processing output data during simulation time and saving predetermined visualizations in image databases. This poster proposes a visual analytics approach to exploring and analyzing image-based simulation ensembles, taking advantage of semantic interaction, feature extraction, and deep learning techniques. Our approach uses deep learning and local feature techniques to learn image features and pass them along with the input parameters to the visualization pipeline for in-depth exploration and analysis of parameter and ensemble spaces simultaneously.
Explorative Visual Analysis of Spatio-temporal Regions to Detect Hemodynamic Biomarker Candidates
(The Eurographics Association, 2022) Derstroff, Adrian; Leistikow, Simon; Nahardani, Ali; Ebrahimi, Mahyasadat; Hoerr, Verena; Linsen, Lars; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Biomarkers are measurable biological properties that allow for distinguishing subjects of different cohorts such as healthy vs. diseased. In the context of diagnosing diseases of the cardiovascular system, researchers aim - among others - at detecting biomarkers in the form of spatio-temporal regions of blood flow obtained by medical imaging or of derived hemodynamical parameters. As the search space for such biomarkers in time-varying volumetric multi-field data is extremely large, we present an interactive visual exploration system to support the analysis of the potential of spatio-temporal regions to discriminate cohorts.
GDot-i: Interactive System for Dot Paintings of Graphs
(The Eurographics Association, 2022) Eades, Peter; Hong, Seok-Hee; McGrane, Martin; Meidiana, Amyra; Krone, Michael; Lenti, Simone; Schmidt, Johanna
This poster presents GDot-i, an interactive system visualizing graphs and networks as dot paintings, inspired by the dot painting style of Central Australia. We describe the implementation of GDot-i, a web-based interactive system, including the user interface and typical use cases.
Interactive Attribution-based Explanations for Image Segmentation
(The Eurographics Association, 2022) Humer, Christina; Elharty, Mohamed; Hinterreiter, Andreas; Streit, Marc; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Explanations of deep neural networks (DNNs) give users a better understanding of the inner workings and generalizability of a network. While the majority of research focuses on explanations for classification networks, in this work we focus on explainability for image segmentation networks. As a first contribution, we introduce a lightweight framework that allows generalizing certain attribution-based explanations, originally developed for classification networks, to also work for segmentation networks. The second contribution is a web-based tool that utilizes this framework and allows users to interactively explore segmentation networks. We demonstrate the approach using a self-trained mushroom segmentation network.
Interactive Visualization of Machine Learning Model Results Predicting Infection Risk
(The Eurographics Association, 2022) Schäfer, Steffen; Baumgartl, Tom; Wulff, Antje; Kuijper, Arjan; Marschollek, Michael; Scheithauer, Simone; von Landesberger, Tatiana; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present a novel visual-interactive interface to show results of a machine learning algorithm, which predicts the infection probability for patients in hospitals. The model result data is complex and needs to be presented in a clear and intuitive way to microbiology and infection control experts in hospitals. Our visual-interactive interface offers linked views which allow for detailed analysis of the model results. Feedback from microbiology and infection control experts showed that they were able to extract new insights regarding outbreaks and transmission pathways.
A Mental Workload Estimation for Visualization Evaluation Using EEG Data and NASA-TLX
(The Eurographics Association, 2022) Yim, Soobin; Yoon, Chanyoung; Yoo, Sangbong; Jang, Yun; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Mental workload is a cognitive effort felt by users while solving tasks, and good visualizations tend to induce a low mental workload. For better visualizations, various visualization techniques have been evaluated through quantitative methods that compare the response accuracy and performance time for completing visualization tasks. However, accuracy and time do not always represent the mental workload of a subject. Since quantitative approaches do not fully mirror mental workload, questionnaires and biosignals have been employed to measure mental workload in visualization assessments. The electroencephalogram (EEG) as biosignal is one of the indicators frequently utilized to measure mental workload. Since everyone judges and senses differently, EEG signals and mental workload differ from person to person. In this paper, we propose a mental workload personalized estimation model with EEG data specialized for each individual to evaluate visualizations. We use scatter plot, bar, line, and map visualizations and collect NASA-TLX scores as mental workload and EEG data. NASA-TLX and EEG data as training data are used for the mental workload estimation model.
MOBS - Multi-Omics Brush for Subgraph Visualisation
(The Eurographics Association, 2022) Heylen, Dries; Peeters, Jannes; Ertaylan, Gökhan; Hooyberghs, Jef; Aerts, Jan; Krone, Michael; Lenti, Simone; Schmidt, Johanna
One of the big opportunities in multi-omics analysis is the identification of interactions between molecular entities and their association with diseases. In analyzing and expressing these interactions in the search for new hypotheses, multi-omics data is often either translated into matrices containing pairwise correlations and distances, or visualized as node-link diagrams. A major problem when visualizing large networks however is the occurrence of hairball-like graphs, from which little to none information can be extracted. It is of interest to investigate subgroups of markers that are closely associated with each other, rather than just looking at the overload of all interactions. Hence, we propose MOBS (Multi-Omics Brush for Subgraph visualisation), a web-based visualisation interface that can provide both an overview and detailed views on the data. By means of a two dimensional brush on a heatmap that includes hierarchical clustering, relationships of interest can be extracted from a fully connected graph, to enable detailed analysis of the subgraph of interest.
On Visualizing Music Storage Media for Modern Access to Historic Sources
(The Eurographics Association, 2022) Khulusi, Richard; Fricke, Heike; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Finding a balance between conserving historic objects and using them for research is one of the big issues in historic collections. Digitization holds the opportunity to offer a safe and non-destructible access to historic objects, making them available for research. With this poster, we want to give insight into our planned visualization system, using close and distant reading access for visual analysis approaches and allowing musicologists novel approaches to normally fragile and endangered media.
Parameter Sensitivity and Uncertainty Visualization in DTI
(The Eurographics Association, 2022) Siddiqui, Faizan; Höllt, Thomas; Vilanova, Anna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Diffusion Tensor Imaging is a powerful technique that provides a unique insight into the complex structure of the brain's white matter. However, several sources of uncertainty limit its widespread use. Data and modeling errors arise due to acquisition noise and modeling transformations. Moreover, the sensitivities of the user-defined parameters and region definitions are not usually evaluated, a small change in these parameters can add large variations in the results. Without showing these uncertainties any visualization of DTI data can potentially be misleading. In our work, we develop a visual analytic tool that provides insight into the accumulated uncertainty in the visualization pipeline. The primary goal of this project is to develop an efficient visualization strategy that will assist the end-user in making critical decisions and make fiber tracking analysis less cumbersome and more reliable, a crucial step towards adoption in the neurosurgical workflow.
PSEUDo: Interactive Pattern Search in Multivariate Time Series with Locality-Sensitive Hashing and Relevance Feedback
(The Eurographics Association, 2022) Yu, Yuncong; Kruyff, Dylan; Jiao, Jiao; Becker, Tim; Behrisch, Michael; Krone, Michael; Lenti, Simone; Schmidt, Johanna
We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome the uneconomic (re- )training with deep learning-based methods. Very high-dimensional time series emerge on an unprecedented scale due to increasing sensor usage and data storage. Visual pattern search is one of the most frequent tasks on such data. Automatic pattern retrieval methods often suffer from inefficient training, a lack of ground truth, and a discrepancy between the similarity perceived by the algorithm and the user. Our proposal is based on a query-aware locality-sensitive hashing technique to create a representation of multivariate time series windows. It features sub-linear training and inference time with respect to data dimensions. This performance gain allows an instantaneous relevance-feedback-driven adaption and converges to users' similarity notion. We are benchmarking PSEUDo in accuracy and speed with representative and state-of-the-art methods, evaluating its steerability through simulated user behavior, and designing expert studies to test PSEUDo's usability.

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