Context Aware Terrain Visualization for Wayfinding and Navigation

To assist wayfinding and navigation, the display of maps and driving directions on mobile devices is nowadays commonplace. While existing system can naturally exploit GPS information to facilitate orientation, the inherently limited screen space is often perceived as a drawback compared to traditional street maps as it constrains the perception of contextual information. Moreover, occlusion issues add to this problem if the environment is shown from the popular egocentric perspective. In this paper we describe an interactive visualization system that addresses these problems by reallocating the available screen space. At the heart of our system are three novel visualization techniques: First, we propose a non‐standard perspective that allows to blend between the familiar pedestrian perspective and a standard map depiction with reduced occlusion. Second, we derive an efficient deformation technique that allows an interactive allocation of screen space to areas of interest like e.g. nearby touristic attractions. Finally, a path adaptive isometric perspective is proposed that reveals otherwise hidden facades in top‐down views. We describe efficient implementations of all techniques and exemplify our interactive system on real world urban models.     

Dynamic Insets for Context‐Aware Graph Navigation

Maintaining both overview and detail while navigating in graphs, such as road networks, airline route maps, or social networks, is difficult, especially when targets of interest are located far apart. We present a navigation technique called Dynamic Insets that provides context awareness for graph navigation. Dynamic insets utilize the topological structure of the network to draw a visual inset for off‐screen nodes that shows a portion of the surrounding area for links leaving the edge of the screen. We implement dynamic insets for general graph navigation as well as geographical maps. We also present results from a set of user studies that show that our technique is more efficient than most of the existing techniques for graph navigation in different networks.     

Constructive Visual Analytics for Text Similarity Detection

Detecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th‐century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.     

Image Appearance Exploration by Model-Based Navigation

Changing the appearance of an image can be a complex and non-intuitive task. Many times the target image colors and look are only known vaguely and many trials are needed to reach the desired results. Moreover, the effect of a specific change on an image is difficult to envision, since one must take into account spatial image considerations along with the color constraints. Tools provided today by image processing applications can become highly technical and non-intuitive including various gauges and knobs.
In this paper we introduce a method for changing image appearance by navigation, focusing on recoloring images. The user visually navigates a high dimensional space of possible color manipulations of an image. He can either explore in it for inspiration or refine his choices by navigating into sub regions of this space to a specific goal. This navigation is enabled by modeling the chroma channels of an image's colors using a Gaussian Mixture Model (GMM). The Gaussians model both color and spatial image coordinates, and provide a high dimensional parameterization space of a rich variety of color manipulations. The user's actions are translated into transformations of the parameters of the model, which recolor the image. This approach provides both inspiration and intuitive navigation in the complex space of image color manipulations.     

Towards closing the analysis gap: Visual generation of decision supporting schemes from raw data

The derivation, manipulation and verification of analytical models from raw data is a process which requires a transformation of information across different levels of abstraction. We introduce a concept for the coupling of data classification and interactive visualization in order to make this transformation visible and steerable for the human user. Data classification techniques generate mappings that formally group data items into categories. Interactive visualization includes the user into an iterative refinement process. The user identifies and selects interesting patterns to define these categories. The following step is the transformation of a visible pattern into the formal definition of a classifier. In the last step the classifier is transformed back into a pattern that is blended with the original data in the same visual display. Our approach allows in intuitive assessment of a formal classifier and its model, the detection of outliers and the handling of noisy data using visual pattern‐matching. We instantiated the concept using decision trees for classification and KVMaps as the visualization technique. The generation of a classifier from visual patterns and its verification is transformed from a cognitive to a mostly pre‐cognitive task.     

Reusable Visualizations and Animations for Surgery Planning

For surgical planning, the exploration of 3D visualizations and 2D slice views is essential. However, the generation of visualizations which support the specific treatment decisions is very tedious. Therefore, the reuse of once designed visualizations for similar cases can strongly accelerate the process of surgical planning. We present a new technique that enables the easy reuse of both medical visualization types: 3D scenes and 2D slice views. We introduce the keystates as a concept to describe the state of a visualization in a general manner. They can be easily applied to new datasets to create similar visualizations. Keystates can be shared between surgeons of one specialization to reproduce and document the planning process for collaborative work. Furthermore, animations can support the surgeon on individual exploration and are also useful in collaborative environments, where complex issues must be presented in a short time. Therefore, we provide a framework, where animations can be visually designed by surgeons during their exploration process without any programming or authoring skills. We discuss several transitions between different visualizations and present an application from clinical routine.     

Visual Analysis of Time‐Series Similarities for Anomaly Detection in Sensor Networks

We present a system to analyze time‐series data in sensor networks. Our approach supports exploratory tasks for the comparison of univariate, geo‐referenced sensor data, in particular for anomaly detection. We split the recordings into fixed‐length patterns and show them in order to compare them over time and space using two linked views. Apart from geo‐based comparison across sensors we also support different temporal patterns to discover seasonal effects, anomalies and periodicities. The methods we use are best practices in the information visualization domain. They cover the daily, the weekly and seasonal and patterns of the data. Daily patterns can be analyzed in a clustering‐based view, weekly patterns in a calendar‐based view and seasonal patters in a projection‐based view. The connectivity of the sensors can be analyzed through a dedicated topological network view. We assist the domain expert with interaction techniques to make the results understandable. As a result, the user can identify and analyze erroneous and suspicious measurements in the network. A case study with a domain expert verified the usefulness of our approach.     

Visual Support for Interactive Post‐Interventional Assessment of Radiofrequency Ablation Therapy

Percutaneous radiofrequency (RF) ablation is a minimally invasive, image‐guided therapy for the treatment of liver tumors. The assessment of the ablation area (coagulation) is performed to verify the treatment success as an essential part of the therapy. Traditionally, pre‐ and post‐interventional CT images are used to visually compare the shape, size, and position of tumor and coagulation. In this work, we present a novel visualization as well as a navigation tool, the so‐called tumor map. The tumor map is a pseudo‐cylindrical mapping of the tumor surface onto a 2D image. It is used for a combined visualization of all ablation zones of the tumor to allow a reliable therapy assessment. Additionally, the tumor map serves as an interactive tool for intuitive navigation within the 3D volume rendering of the tumor vicinity as well as with familiar 2D viewers.     

Visual Abstractions of Solvent Pathlines near Protein Cavities

Water is known to play a crucial role in protein structure, flexibility and activity. The use of molecular dynamics simulations allows detailed studies of complex protein‐solvent interactions. Cluster analysis and density‐based approaches have been successfully used for the identification and analysis of conserved water molecules and hydration patterns of proteins. However, appropriate tools for analysing long‐time molecular dynamics simulations with respect to tracking and visualising the paths of solvent molecules are lacking. Our method focuses on visualising the solvent paths entering and leaving cavities of the protein and allows to study the route and dynamics of the exchange of tightly bound internal water molecules with the bulk solvent. The proposed visualisation also represents dynamic properties such as direction and velocity in the solvent. Especially, by clustering similar path‐lines with respect to designated properties the visualisation can be abstracted to represent the principal paths of solvent molecules through the cavities. Its application in the analysis of long‐time scale molecular dynamics simulations not only confirmed conjectures based on previous manual observations made by chance, but also led to novel insights into the dynamical and structural role of water molecules and its interplay with protein structure.     

Detangler: Visual Analytics for Multiplex Networks

A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.     

Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting

Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision‐making and process‐quality measurements.     

COPERNICUS: Context‐Preserving Engine for Route Navigation with Interactive User‐modifiable Scaling

In this paper, we present an automated system for generating context‐preserving route maps that depict navigation routes as a path between nodes and edges inside a topographic network. Our application identifies relevant context information to support navigation and orientation, and generates customizable route maps according to design principles that communicate all relevant context information clearly visible on one single page. Interactive scaling allows seamless transition between the original undistorted map and our new map design, and supports user‐specified scaling of regions of interest to create personalized driving directions according to the drivers needs.     

Visual Boosting in Pixel‐based Visualizations

Pixel‐based visualizations have become popular, because they are capable of displaying large amounts of data and at the same time provide many details. However, pixel‐based visualizations are only effective if the data set is not sparse and the data distribution not random. Single pixels – no matter if they are in an empty area or in the middle of a large area of differently colored pixels – are perceptually difficult to discern and may therefore easily be missed. Furthermore, trends and interesting passages may be camouflaged in the sea of details. In this paper we compare different approaches for visual boosting in pixel‐based visualizations. Several boosting techniques such as halos, background coloring, distortion, and hatching are discussed and assessed with respect to their effectiveness in boosting single pixels, trends, and interesting passages. Application examples from three different domains (document analysis, genome analysis, and geospatial analysis) show the general applicability of the techniques and the derived guidelines.     

Smart Motion Synthesis

Creating long motion sequences is a time‐consuming task even when motion capture equipment or motion editing tools are used. In this paper, we propose a system for creating a long motion sequence by combining elementary motion clips. The user is asked to first input motions on a timeline. The system then automatically generates a continuous and natural motion. Our system employs four motion synthesis methods: motion transition, motion connection, motion adaptation, and motion composition. Based on the constraints between the feet of the animated character and the ground, and the timing of the input motions, the appropriate method is determined for each pair of overlapped or sequential motions. As the user changes the arrangement of the motion clips, the system interactively changes the output motion. Alternatively, the user can make the system execute an input motion as soon as possible so that it follows the previous motion smoothly. Using our system, users can make use of existing motion clips. Because the entire process is automatic, even novices can easily use our system. A prototype system demonstrates the effectiveness of our approach.     

Visualization of Neural Network Predictions for Weather Forecasting

Recurrent neural networks are prime candidates for learning evolutions in multi‐dimensional time series data. The performance of such a network is judged by the loss function, which is aggregated into a scalar value that decreases during training. Observing only this number hides the variation that occurs within the typically large training and testing data sets. Understanding these variations is of highest importance to adjust network hyper‐parameters, such as the number of neurons, number of layers or to adjust the training set to include more representative examples. In this paper, we design a comprehensive and interactive system that allows users to study the output of recurrent neural networks on both the complete training data and testing data. We follow a coarse‐to‐fine strategy, providing overviews of annual, monthly and daily patterns in the time series and directly support a comparison of different hyper‐parameter settings. We applied our method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, pressure and wind velocity. We further visualize the quality of the forecasting models, when applied to various locations on the Earth and we examine the combination of several forecasting models.     

The Visual Computing of Projector‐Camera Systems

This article focuses on real‐time image correction techniques that enable projector‐camera systems to display images onto screens that are not optimized for projections, such as geometrically complex, coloured and textured surfaces. It reviews hardware‐accelerated methods like pixel‐precise geometric warping, radiometric compensation, multi‐focal projection and the correction of general light modulation effects. Online and offline calibration as well as invisible coding methods are explained. Novel attempts in super‐resolution, high‐dynamic range and high‐speed projection are discussed. These techniques open a variety of new applications for projection displays. Some of them will also be presented in this report.     

SDViz: A Context-Preserving Interactive Visualization System for Technical Diagrams

When performing daily maintenance and repair tasks, technicians require access to a variety of technical diagrams. As technicians trace components and diagrams from page-to-page, within and across manuals, the contextual information of the components they are analyzing can easily be lost. To overcome these issues, we have developed a Schematic Diagram Visualization System (SDViz) designed for maintaining and highlighting contextual information in technical documents, such as schematic and wiring diagrams. Our system incorporates various features to aid in the navigation and diagnosis of faults, as well as maintaining contextual information when tracing components/connections through multiple diagrams. System features include highlighting relationships between components and connectors, diagram annotation tools, the animation of flow through the system, a novel contextual blending method, and a variety of traditional focus+context visualization techniques. We have evaluated the usefulness of our system through a qualitative user study in which subjects utilized our system in diagnosing faults during a standard aircraft maintenance exercise.     

A Gradient‐Based Comparison Measure for Visual analysis of Multifield Data

We introduce a multifield comparison measure for scalar fields that helps in studying relations between them. The comparison measure is insensitive to noise in the scalar fields and to noise in their gradients. Further, it can be computed robustly and efficiently. Results from the visual analysis of various data sets from climate science and combustion applications demonstrate the effective use of the measure.     

Illustrative Parallel Coordinates

Illustrative parallel coordinates (IPC) is a suite of artistic rendering techniques for augmenting and improving parallel coordinate (PC) visualizations. IPC techniques can be used to convey a large amount of information about a multidimensional dataset in a small area of the screen through the following approaches: (a) edge‐bundling through splines; (b) visualization of “branched ” clusters to reveal the distribution of the data; (c) opacity‐based hints to show cluster density; (d) opacity and shading effects to illustrate local line density on the parallel axes; and (e) silhouettes, shadows and halos to help the eye distinguish between overlapping clusters. Thus, the primary goal of this work is to convey as much information as possible in a manner that is aesthetically pleasing and easy to understand for non‐experts.