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.     

Multi-focused geospatial analysis using probes

Traditional geospatial information visualizations often present views that restrict the user to a single perspective. When zoomed out, local trends and anomalies become suppressed and lost; when zoomed in for local inspection, spatial awareness and comparison between regions become limited. In our model, coordinated visualizations are integrated within individual probe interfaces, which depict the local data in user-defined regions-of-interest. Our probe concept can be incorporated into a variety of geospatial visualizations to empower users with the ability to observe, coordinate, and compare data across multiple local regions. It is especially useful when dealing with complex simulations or analyses where behavior in various localities differs from other localities and from the system as a whole. We illustrate the effectiveness of our technique over traditional interfaces by incorporating it within three existing geospatial visualization systems: an agent-based social simulation, a census data exploration tool, and an 3D GIS environment for analyzing urban change over time. In each case, the probe-based interaction enhances spatial awareness, improves inspection and comparison capabilities, expands the range of scopes, and facilitates collaboration among multiple users.     

CoViCAD: comprehensive visualization of coronary artery disease

We present novel, comprehensive visualization techniques for the diagnosis of patients with Coronary Artery Disease using segmented cardiac MRI data. We extent an accepted medical visualization technique called the bull's eye plot by removing discontinuities, preserving the volumetric nature of the left ventricular wall and adding anatomical context. The resulting volumetric bull's eye plot can be used for the assessment of transmurality. We link these visualizations to a 3D view that presents viability information in a detailed anatomical context. We combine multiple MRI scans (whole heart anatomical data, late enhancement data) and multiple segmentations (polygonal heart model, late enhancement contours, coronary artery tree). By selectively combining different rendering techniques we obtain comprehensive yet intuitive visualizations of the various data sources.     

Lessons learned: Evaluating visualizations for occluded objects in handheld augmented reality

Handheld devices like smartphones and tablets have emerged as one of the most promising platforms for Augmented Reality (AR). The increased usage of these portable handheld devices has enabled handheld AR applications to reach the end-users; hence, it is timely and important to seriously consider the user experience of such applications. AR visualizations for occluded objects enable an observer to look through objects. AR visualizations have been predominantly evaluated using Head-Worn Displays (HWDs), handheld devices have rarely been used. However, unless we gain a better understanding of the perceptual and cognitive effects of handheld AR systems, effective interfaces for handheld devices cannot be designed. Similarly, human perception of AR systems in outdoor environments, which provide a higher degree of variation than indoor environments, has only been insufficiently explored.In this paper, we present insights acquired from five experiments we performed using handheld devices in outdoor locations. We provide design recommendations for handheld AR systems equipped with visualizations for occluded objects. Our key conclusions are the following: (1) Use of visualizations for occluded objects improves the depth perception of occluded objects akin to non-occluded objects. (2) To support different scenarios, handheld AR systems should provide multiple visualizations for occluded objects to complement each other. (3) Visual clutter in AR visualizations reduces the visibility of occluded objects and deteriorates depth judgment; depth judgment can be improved by providing clear visibility of the occluded objects. (4) Similar to virtual reality interfaces, both egocentric and exocentric distances are underestimated in handheld AR. (5) Depth perception will improve if handheld AR systems can dynamically adapt their geometric field of view (GFOV) to match the display field of view (DFOV). (6) Large handheld displays are hard to carry and use; however, they enable users to better grasp the depth of multiple graphical objects that are presented simultaneously.     

Multilayer hybrid visualizations to support 3D GIS

In this paper, we present a unique hybrid visualization system for spatial data. Although some existing 3D GIS systems offer 2D views they are typically isolated from the 3D view in that they are presented in a separate window. Our system is a novel hybrid 2D/3D approach that seamlessly integrates 2D and 3D views of the same data. In our interface, multiple layers of information are continuously transformed between the 2D and 3D modes under the control of the user, directly over a base terrain. In this way, our prototype system is able to depict 2D and 3D views within the same window. This has advantages, since 2D and 3D visualizations can each be easier to interpret in different contexts.In this work we develop this concept of a hybrid visualization by presenting a comprehensive set of capabilities within our distinctive system. These include new facilities such as: hybrid landmark, 3D point, and chart layers, the grouping of multiple hybrid layers, layer painting, the merging of layer controls and consistent zooming functionality.     

Hyper-Radial Visualization (HRV) method with range-based preferences for multi-objective decision making

A visualization-based methodology is developed in which a Hyperspace Pareto Frontier (HPF) can be represented for design concept selection. The new approach is termed the Hyper-Radial Visualization (HRV) method. The HRV method enables designers to investigate trade-off decisions between Pareto solutions by their relative position in an HRV-based visualization. Three a posteri range-based preference incorporation approaches are proposed in this paper that can be combined with HRV-based visualizations to enable designers to quickly identify better regions in high dimensional performance space for Multi-objective Optimization Problems (MOPs). The paper first explains the details of the HRV method, which can generate a meaningful representation of an HPF. Second, three color-coding preference schemes are proposed in this work to enable intuitive trade-off studies using the HRV-based HPF visualizations. Finally, several MOPs are used to investigate the performance of the HRV-based preference approaches that have been proposed. The viability and desirability of using the HRV for decision making support is also explored.     

Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering

Web‐based visualization libraries are in wide use, but performance bottlenecks occur when rendering, and especially animating, a large number of graphical marks. While GPU‐based rendering can drastically improve performance, that paradigm has a steep learning curve, usually requiring expertise in the computer graphics pipeline and shader programming. In addition, the recent growth of virtual and augmented reality poses a challenge for supporting multiple display environments beyond regular canvases, such as a Head Mounted Display (HMD) and Cave Automatic Virtual Environment (CAVE). In this paper, we introduce a new web‐based visualization library called Stardust, which provides a familiar API while leveraging GPU's processing power. Stardust also enables developers to create both 2D and 3D visualizations for diverse display environments using a uniform API. To demonstrate Stardust's expressiveness and portability, we present five example visualizations and a coding playground for four display environments. We also evaluate its performance by comparing it against the standard HTML5 Canvas, D3, and Vega.     

视景仿真中三维地球的建模

给出了一种基于Blue Marble纹理和GTOPO30高程数据的三维地球建模方法，可消除地表各分块间的缝隙。地表形状模拟精度高，结合多分辨率纹理和高程，实现了对地球的变视角和缩放操作，在飞行模拟、导弹发射、卫星绕地球飞行等涉及到地球大场景或全球视景的仿真中被广泛采用。该建模方法对类似的建模提供了一种新思路，有较高的通用性和实用性。     

Stereoscopic highlighting: 2D graph visualization on stereo displays

In this paper we present a new technique and prototype graph visualization system, stereoscopic highlighting, to help answer accessibility and adjacency queries when interacting with a node-link diagram. Our technique utilizes stereoscopic depth to highlight regions of interest in a 2D graph by projecting these parts onto a plane closer to the viewpoint of the user. This technique aims to isolate and magnify specific portions of the graph that need to be explored in detail without resorting to other highlighting techniques like color or motion, which can then be reserved to encode other data attributes. This mechanism of stereoscopic highlighting also enables focus+context views by juxtaposing a detailed image of a region of interest with the overall graph, which is visualized at a further depth with correspondingly less detail. In order to validate our technique, we ran a controlled experiment with 16 subjects comparing static visual highlighting to stereoscopic highlighting on 2D and 3D graph layouts for a range of tasks. Our results show that while for most tasks the difference in performance between stereoscopic highlighting alone and static visual highlighting is not statistically significant, users performed better when both highlighting methods were used concurrently. In more complicated tasks, 3D layout with static visual highlighting outperformed 2D layouts with a single highlighting method. However, it did not outperform the 2D layout utilizing both highlighting techniques simultaneously. Based on these results, we conclude that stereoscopic highlighting is a promising technique that can significantly enhance graph visualizations for certain use cases.     

SparkClouds: visualizing trends in tag clouds

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations—multiple line graphs and stacked bar charts—as well as Parallel Tag Clouds. Results show that SparkClouds ability to show trends compares favourably to the alternative visualizations.     

Learning about locomotion patterns: Effective use of multiple pictures and motion-indicating arrows

This study investigated how enriching visualizations with arrows indicating the motion of objects may help in conveying dynamic information: Multiple static-simultaneous visualizations with motion-indicating arrows were compared with either multiple visualizations without arrows or a single visualization with arrows. Seventy-one students were randomly assigned to the three conditions. Learning outcomes were measured by dynamic and static pictorial test items requiring learners to classify objects according to their locomotion pattern. Even though learners' performance in the static test indicated some use of static cues, they showed better performance once dynamic information was available suggesting that it is applied to accomplish the task. Unexpectedly, the combined condition performed worse than both other conditions in the dynamic test. Accordingly, multiple visualizations without arrows and single visualizations with motion indicators appear to facilitate mental animation differently either by supporting comparisons among multiple pictures or by making dynamic information explicit. However, combining both instructional methods may result in interference thereby hindering learning.     

A cartographic approach to visualizing conference abstracts

A cartographic approach to mapping nongeographic information helps to manage graphic complexity in visualizations. It aids domain comprehension by forcing us to use the same cognitive skills we use when viewing geographic maps. The author presents a distinctly cartographic approach to mapping nongeographic information. Focusing on the text content of a set of conference abstracts, we can derive 2D visualizations of information spaces that address complexity and automation     

Data visualization optimization via computational modeling of perception

We present a method for automatically evaluating and optimizing visualizations using a computational model of human vision. The method relies on a neural network simulation of early perceptual processing in the retina and primary visual cortex. The neural activity resulting from viewing flow visualizations is simulated and evaluated to produce a metric of visualization effectiveness. Visualization optimization is achieved by applying this effectiveness metric as the utility function in a hill-climbing algorithm. We apply this method to the evaluation and optimization of 2D flow visualizations, using two visualization parameterizations: streaklet-based and pixel-based. An emergent property of the streaklet-based optimization is head-to-tail streaklet alignment. It had been previously hypothesized the effectiveness of head-to-tail alignment results from the perceptual processing of the visual system, but this theory had not been computationally modeled. A second optimization using a pixel-based parameterization resulted in a LIC-like result. The implications in terms of the selection of primitives is discussed. We argue that computational models can be used for optimizing complex visualizations. In addition, we argue that they can provide a means of computationally evaluating perceptual theories of visualization, and as a method for quality control of display methods.     

Explanatory and illustrative visualization of special and general relativity

This paper describes methods for explanatory and illustrative visualizations used to communicate aspects of Einstein's theories of special and general relativity, their geometric structure, and of the related fields of cosmology and astrophysics. Our illustrations target a general audience of laypersons interested in relativity. We discuss visualization strategies, motivated by physics education and the didactics of mathematics, and describe what kind of visualization methods have proven to be useful for different types of media, such as still images in popular science magazines, film contributions to TV shows, oral presentations, or interactive museum installations. Our primary approach is to adopt an egocentric point of view: the recipients of a visualization participate in a visually enriched thought experiment that allows them to experience or explore a relativistic scenario. In addition, we often combine egocentric visualizations with more abstract illustrations based on an outside view in order to provide several presentations of the same phenomenon. Although our visualization tools often build upon existing methods and implementations, the underlying techniques have been improved by several novel technical contributions like image-based special relativistic rendering on GPUs, special relativistic 4D ray tracing for accelerating scene objects, an extension of general relativistic ray tracing to manifolds described by multiple charts, GPU-based interactive visualization of gravitational light deflection, as well as planetary terrain rendering. The usefulness and effectiveness of our visualizations are demonstrated by reporting on experiences with, and feedback from, recipients of visualizations and collaborators.     

Learning with dynamic and static visualizations: Realistic details only benefit learners with high visuospatial abilities

Learning environments can nowadays easily be enriched with different presentation formats of visualizations, because computer graphics technology is constantly and rapidly developing. This study investigates the effectiveness of dynamic compared to static visualizations. Moreover, the influence of realistic details in the visualizations as well as learners’ prerequisites in terms of their visuospatial abilities were addressed. Eighty university students were randomly assigned to four conditions of a two-by-two between subjects design with the two independent variables dynamism and realism. Learning outcomes were measured by means of a verbal factual knowledge test about the terminology and visuospatial details and a pictorial recognition test about the dynamic processes. Data analyses revealed no effects for factual knowledge. With respect to recognition, learners with dynamic visualizations outperformed learners with static visualizations. Furthermore, there was an interaction between learners’ visuospatial abilities and the degree of realism in the visualization: learners with lower visuospatial abilities showed better recognition with schematized visualizations, whereas learners with higher visuospatial abilities showed better recognition with realistic visualizations. The results imply that when designing instructional materials, both the type of knowledge that has to be acquired as well as learners’ prerequisites such as their visuospatial abilities need to be considered.     

Comparing Node‐Link and Node‐Link‐Group Visualizations From An Enjoyment Perspective

While evaluation studies in visualization often involve traditional performance measurements, there has been a concerted effort to move beyond time and accuracy. Of these alternative aspects, memorability and recall of visualizations have been recently considered, but other aspects such as enjoyment and engagement are not as well explored. We study the enjoyment of two different visualization methods through a user study. In particular, we describe the results of a three‐phase experiment comparing the enjoyment of two different visualizations of the same relational data: node‐link and node‐link‐group visualizations. The results indicate that the participants in this study found node‐link‐group visualizations more enjoyable than node‐link visualizations.     

Worm plots

Scientists sometimes spend hours conducting experiments only to find that the resulting data proves difficult to analyze with traditional visualization techniques. A typical laboratory experiment, for example, will setup several systems in each of two or more groups-a control group and various treatment groups. The investigator will then measure various parameters for each system over time (sometimes dozens to hundreds of parameters). The investigator tries to answer this question: What is different between the control and treatment groups? Field monitoring of polluted and unpolluted sites within a region result in the same kinds of data and the same difficulties in visualization. Plotting multiple lines on a single 2D plot quickly gets confusing. Plus, the dynamic interactions between parameters can be hard to see. 3D visualizations help researchers make qualitative insights about data more easily. We developed a 3D plotting technique, called “worm plots”, for visualizing these kinds of data. Our visualization tool examines how groups of points change over time. If we plot circles for each time slice, then these circles will change their relative sizes and positions as time goes by. To visualize these evolving dynamics, we connect these time slices with conic sections. This gives us spacetime worms that can be displayed graphically. In our experience, these simple circular cross sections work best when exploring data. They can be rendered quickly and give an easily intuited summary of each group. Further enhancements tend to make the image busy and difficult to interpret     

An approach to the perceptual optimization of complex visualizations

This paper proposes a new experimental framework within which evidence regarding the perceptual characteristics of a visualization method can be collected, and describes how this evidence can be explored to discover principles and insights to guide the design of perceptually near-optimal visualizations. We make the case that each of the current approaches for evaluating visualizations is limited in what it can tell us about optimal tuning and visual design. We go on to argue that our new approach is better suited to optimizing the kinds of complex visual displays that are commonly created in visualization. Our method uses human-in-the-loop experiments to selectively search through the parameter space of a visualization method, generating large databases of rated visualization solutions. Data mining is then used to extract results from the database, ranging from highly specific exemplar visualizations for a particular data set, to more broadly applicable guidelines for visualization design. We illustrate our approach using a recent study of optimal texturing for layered surfaces viewed in stereo and in motion. We show that a genetic algorithm is a valuable way of guiding the human-in-the-loop search through visualization parameter space. We also demonstrate several useful data mining methods including clustering, principal component analysis, neural networks, and statistical comparisons of functions of parameters.     

VizAssist: an interactive user assistant for visual data mining

We study in this work how a user can be guided to find a relevant visualization in the context of visual data mining. We present a state of the art on the user assistance in visual and interactive methods. We propose a user assistant called VizAssist, which aims at improving the existing approaches along three directions: it uses simpler computational models of the visualizations and the visual perception guidelines, in order to facilitate the integration of new visualizations and the definition of a mapping heuristic. VizAssist allows the user to provide feedback in a visual and interactive way, with the aim of improving the data to visualization mapping. This step is performed with an interactive genetic algorithm. Finally, VizAssist aims at proposing a free on-line tool (www.vizassist.fr) that respects the privacy of the user data. This assistant can be viewed as a global interface between the user and some of the many visualizations that are implemented with D3js.