Feature Tracking by Two-Step Optimization.

Tracking the temporal evolution of features in time-varying data is a key method in visualization. For typical feature definitions, such as vortices, objects are sparsely distributed over the data domain. In this paper, we present a novel approach for tracking both sparse and space-filling features. While the former comprise only a small fraction of the domain, the latter form a set of objects whose union covers the domain entirely while the individual objects are mutually disjunct. Our approach determines the assignment of features between two successive time-steps by solving two graph optimization problems. It first resolves one-to-one assignments of features by computing a maximum-weight, maximum-cardinality matching on a weighted bi-partite graph. Second, our algorithm detects events by creating a graph of potentially conflicting event explanations and finding a weighted, independent set in it. We demonstrate our method's effectiveness on synthetic and simulation data sets, the former of which enables quantitative evaluation because of the availability of ground-truth information. Here, our method performs on par or better than a well-established reference algorithm. In addition, manual visual inspection by our collaborators confirm the results' plausibility for simulation data.

In Situ Visualization for Computational Science.

In situ visualization is an increasingly important approach for computational science, as it can address limitations on leading edge high-performance computers and also can provide an increased spatio-temporal resolution. However, there are many open research issues with effective in situ processing. This article describes the challenges identified by a recent Dagstuhl Seminar on the topic.

Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation.

Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases-the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

Design and Evaluation of Data Annotation Workflows for CAVE-like Virtual Environments.

Data annotation finds increasing use in Virtual Reality applications with the goal to support the data analysis process, such as architectural reviews. In this context, a variety of different annotation systems for application to immersive virtual environments have been presented. While many interesting interaction designs for the data annotation workflow have emerged from them, important details and evaluations are often omitted. In particular, we observe that the process of handling metadata to interactively create and manage complex annotations is often not covered in detail. In this paper, we strive to improve this situation by focusing on the design of data annotation workflows and their evaluation. We propose a workflow design that facilitates the most important annotation operations, i.e., annotation creation, review, and modification. Our workflow design is easily extensible in terms of supported annotation and metadata types as well as interaction techniques, which makes it suitable for a variety of application scenarios. To evaluate it, we have conducted a user study in a CAVE-like virtual environment in which we compared our design to two alternatives in terms of a realistic annotation creation task. Our design obtained good results in terms of task performance and user experience.

Visual Quality Adjustment for Volume Rendering in a Head-Tracked Virtual Environment.

To avoid simulator sickness and improve presence in immersive virtual environments (IVEs), high frame rates and low latency are required. In contrast, volume rendering applications typically strive for high visual quality that induces high computational load and, thus, leads to low frame rates. To evaluate this trade-off in IVEs, we conducted a controlled user study with 53 participants. Search and count tasks were performed in a CAVE with varying volume rendering conditions which are applied according to viewer position updates corresponding to head tracking. The results of our study indicate that participants preferred the rendering condition with continuous adjustment of the visual quality over an instantaneous adjustment which guaranteed for low latency and over no adjustment providing constant high visual quality but rather low frame rates. Within the continuous condition, the participants showed best task performance and felt less disturbed by effects of the visualization during movements. Our findings provide a good basis for further evaluations of how to accelerate volume rendering in IVEs according to user's preferences.

Integrating Visualizations into Modeling NEST Simulations.

Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work.

Quo vadis CAVE: does immersive visualization still matter?

Over 10 years' experience with VR displays, visualization applications, and informal feedback from scientists using these applications has convinced RWTH Aachen University researchers that the combination of full immersion, high image quality, and advanced interaction metaphors makes immersive visualization valuable as an analysis tool in simulation science.

Interactive 3D visualization of structural changes in the brain of a person with corticobasal syndrome.

The visualization of the progression of brain tissue loss in neurodegenerative diseases like corticobasal syndrome (CBS) can provide not only information about the localization and distribution of the volume loss, but also helps to understand the course and the causes of this neurodegenerative disorder. The visualization of such medical imaging data is often based on 2D sections, because they show both internal and external structures in one image. Spatial information, however, is lost. 3D visualization of imaging data is capable to solve this problem, but it faces the difficulty that more internally located structures may be occluded by structures near the surface. Here, we present an application with two designs for the 3D visualization of the human brain to address these challenges. In the first design, brain anatomy is displayed semi-transparently; it is supplemented by an anatomical section and cortical areas for spatial orientation, and the volumetric data of volume loss. The second design is guided by the principle of importance-driven volume rendering: A direct line-of-sight to the relevant structures in the deeper parts of the brain is provided by cutting out a frustum-like piece of brain tissue. The application was developed to run in both, standard desktop environments and in immersive virtual reality environments with stereoscopic viewing for improving the depth perception. We conclude, that the presented application facilitates the perception of the extent of brain degeneration with respect to its localization and affected regions.

Extended pie menus for immersive virtual environments.

Pie menus are a well-known technique for interacting with 2D environments and so far a large body of research documents their usage and optimizations. Yet, comparatively little research has been done on the usability of pie menus in immersive virtual environments (IVEs). In this paper we reduce this gap by presenting an implementation and evaluation of an extended hierarchical pie menu system for IVEs that can be operated with a six-degrees-of-freedom input device. Following an iterative development process, we first developed and evaluated a basic hierarchical pie menu system. To better understand how pie menus should be operated in IVEs, we tested this system in a pilot user study with 24 participants and focus on item selection. Regarding the results of the study, the system was tweaked and elements like check boxes, sliders, and color map editors were added to provide extended functionality. An expert review with five experts was performed with the extended pie menus being integrated into an existing VR application to identify potential design issues. Overall results indicated high performance and efficient design.

Interactive blood damage analysis for ventricular assist devices.

Ventricular Assist Devices (VADs) support the heart in its vital task of maintaining circulation in the human body when the heart alone is not able to maintain a sufficient flow rate due to illness or degenerative diseases. However, the engineering of these devices is a highly demanding task. Advanced modeling methods and computer simulations allow the investigation of the fluid flow inside such a device and in particular of potential blood damage. In this paper we present a set of visualization methods which have been designed to specifically support the analysis of a tensor-based blood damage prediction model. This model is based on the tracing of particles through the VAD, for each of which the cumulative blood damage can be computed. The model's tensor output approximates a single blood cell's deformation in the flow field. The tensor and derived scalar data are subsequently visualized using techniques based on icons, particle visualization, and function plotting. All these techniques are accessible through a Virtual Reality-based user interface, which features not only stereoscopic rendering but also natural interaction with the complex three-dimensional data. To illustrate the effectiveness of these visualization methods, we present the results of an analysis session that was performed by domain experts for a specific data set for the MicroMed DeBakey VAD.

Comparative visualization of human nasal airflows.

The use of computational fluid dynamics allows to simulate a large number of different variations of a general flow phenomenon in a reasonable amount of time. This lays the foundation for large scale comparative studies. However, in order to be able to compare the simulation results effectively, advanced comparison methods are needed. In this paper we describe a set of techniques for the comparison of flow simulation results. All methods are integrated in a virtual reality based prototype and facilitate the interactive exploration of the data domain. As a specific application example the described techniques are used to compare different human nasal cavity flows to each other.