Abstract Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under‐explored. This state‐of‐the‐art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions.

Abstract Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system--HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics.

Existing research suggests that individual personality differences are correlated with a user's speed and accuracy in solving problems with different types of complex visualization systems. We extend this research by isolating factors in personality traits as well as in the visualizations that could have contributed to the observed correlation. We focus on a personality trait known as "locus of control” (LOC), which represents a person's tendency to see themselves as controlled by or in control of external events. To isolate variables of the visualization design, we control extraneous factors such as color, interaction, and labeling. We conduct a user study with four visualizations that gradually shift from a list metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their locus of control and other personality factors. Our findings demonstrate that there is indeed a correlation between the two: participants with an internal locus of control perform more poorly with visualizations that employ a containment metaphor, while those with an external locus of control perform well with such visualizations. These results provide evidence for the externalization theory of visualization. Finally, we propose applications of these findings to adaptive visual analytics and visualization evaluation.

Displays supporting stereoscopy and head-coupled motion parallax can enhance human perception of complex 3D datasets. This has been studied extensively for datasets containing 3D surfaces and 3D networks but less for so volumetric data. Volumetric data is characterized by a heavy presence of transparency, occlusion and highly ambiguous spatial structure. There are many different rendering and visualization algorithms and interactive techniques that enhance perception of volume data and these techniques' effectiveness have been evaluated. However, the effect of VR displays on perception of volume data is less well studied. Therefore, we conduct two experiments on how various display conditions affect a participant's depth perception accuracy of a volumetric dataset. A demographic pre-questionnaire also allows us to separate the accuracy differences between participants with more and less experience with 3D games and VR. Our results show an overall benefit for stereo with head-tracking for enhancing perception of depth in volumetric data. Our study also suggests that familiarity with 3D games and VR type technology affects the users'ability to perceive such data and affects the accuracy boost due to VR displays.

Intuition is an important yet ill-defined factor when designing effective multi-touch interactions. Throughout the research community, there is a lack of consensus regarding both the nature of intuition and, more importantly, how to systematically incorporate it into the design of multi-touch gestural interactions. To strengthen our understanding of intuition, we surveyed various domains to determine the level of consensus among researchers, commercial developers, and the general public regarding which multi-touch gestures are intuitive, and which of these gestures intuitively lead to which interaction outcomes. We reviewed more than one hundred papers regarding multi-touch interaction, approximately thirty of which contained key findings we report herein. Based on these findings, we have constructed a framework of five factors that determine the intuition of multi-touch interactions, including direct manipulation, physics, feedback, previous knowledge, and physical motion. We further provide both design recommendations for multi-touch developers and an evaluation of research problems which remain due to the limitations of present research regarding these factors. We expect our survey and discussion of intuition will raise awareness of its importance, and lead to the active pursuit of intuitive multi-touch interaction design.

Research of graffiti character recognition and retrieval, as a branch of traditional optical character recognition (OCR), has started to gain attention in recent years. We have investigated the special challenge of the graffiti image retrieval problem and propose a series of novel techniques to overcome the challenges. The proposed bounding box framework locates the character components in the graffiti images to construct meaningful character strings and conduct image-wise and semantic-wise retrieval on the strings rather than the entire image. Using real world data provided by the law enforcement community to the Pacific Northwest National Laboratory, we show that the proposed framework outperforms the traditional image retrieval framework with better retrieval results and improved computational efficiency.

Displays supporting stereopsis and head location based motion parallax can enhance human perception of complex three dimensional datasets. This has been demonstrated for datasets containing 3D surfaces and 3D networks. Yet many domains, such as medical imaging, weather and environment simulations and fluid flow, generate complex volumetric data. This poster present results of an initial formal experiment that examines the effectiveness of various display conditions on depth perception of volumetric data. There is an overall benefit for stereoscopy with head-tracking in enhancing depth perception. Further, familiarity with 3D games and VR-like hardware improves the users'ability to perceive such data.

Abstract We present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high‐performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I‐SI, closely integrates data‐driven analytical methods and user‐centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I‐SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I‐SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I‐SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics.

Many text collections with temporal references, such as news corpora and weblogs, are generated to report and discuss real life events. Thus, event-related tasks, such as detecting real life events that drive the generation of the text documents, tracking event evolutions, and investigating reports and commentaries about events of interest, are important when exploring such text collections. To incorporate and leverage human efforts in conducting such tasks, we propose a novel visual analytics approach named EventRiver. EventRiver integrates event-based automated text analysis and visualization to reveal the events motivating the text generation and the long term stories they construct. On the visualization, users can interactively conduct tasks such as event browsing, tracking, association, and investigation. A working prototype of EventRiver has been implemented for exploring news corpora. A set of case studies, experiments, and a preliminary user test have been conducted to evaluate its effectiveness and efficiency.

We present research focused on designing visual analytics (VA) systems for workers in organizational environments. We focus on business analysts and asset managers, who work collaboratively to analyze information and make decisions. Through extensive investigations in two organizational environments, we found that these users struggle with managing and analyzing information from multiple perspectives. Their current tools lack support for aggregating, organizing, and sharing such information. To address their needs, we characterized their analytic workflows, extracted specific key knowledge actions for each task commonly found in these workflows, and designed and evaluated two visual analytics systems that support and encapsulate these knowledge actions. We provide design guidelines that should be used when designing visual analytics systems, and illustrate their effectiveness with two systems built by following them.

Abstract We present a probe‐based interface for the exploration of the results of a geospatial simulation of urban growth. Because our interface allows the user great freedom in how they choose to define regions‐of‐interest to examine and compare, the classic geospatial analytic issue known as the modifiable areal unit problem (MAUP) quickly arises. The user may delineate regions with unseen differences that can affect the fairness of the comparisons made between them. To alleviate this problem, our interface first alerts the user if it detects any potential unfairness between regions when they are selected for comparison. It then presents the dimensions with potential problematic outliers to the user for evaluation. Finally, it provides a number of semi‐automated tools to assist the user in correcting their regions' boundaries to minimize the inequalities they feel could significantly impact their comparisons.

Abstract Bridges deteriorate over their life cycles and require continuous maintenance to ensure their structural integrity, and in turn, the safety of the public. Maintaining bridges is a multi‐faceted operation that requires both domain knowledge and analytics techniques over large data sources. Although most existing bridge management systems (BMS) are very efficient at data storage, they are not as effective at providing analytical capabilities or as flexible at supporting different inspection technologies. In this paper, we present a visual analytics system that extends the capability of current BMSs. Based on a nation‐wide survey and our interviews with bridge managers, we designed our system to be customizable so that it can provide interactive exploration, information correlation, and domain‐oriented data analysis. When tested by bridge managers of the U.S. Department of Transportation, we validated that our system provides bridge managers with the necessary features for performing in‐depth analysis of bridges from a variety of perspectives that are in accordance to their typical workflow.

Abstract This paper presents a digital storytelling approach that generates automatic animations for time‐varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree‐like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time‐varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time‐varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.

Although significant progress has been made toward effective insight discovery in visual sense making approaches, there is a lack of effective and efficient approaches to manage the large amounts of insights discovered. In this paper, we propose a systematic approach to leverage this problem around the concept of facts. Facts refer to patterns, relationships, or anomalies extracted from data under analysis. They are the direct products of visual exploration and permit construction of insights together with user's mental model and evaluation. Different from the mental model, the type of facts that can be discovered from data is predictable and application-independent. Thus it is possible to develop a general fact management framework (FMF) to allow visualization users to effectively and efficiently annotate, browse, retrieve, associate, and exchange facts. Since facts are essential components of insights, it will be feasible to extend FMF to effective insight management in a variety of visual analytics approaches. Toward this goal, we first construct a fact taxonomy that categorizes various facts in multidimensional data and captures their essential attributes through extensive literature survey and user studies. We then propose a conceptual framework of fact management based upon this fact taxonomy. A concrete scenario of visual sense making on real data sets illustrates how this FMF will work.

Abstract Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a “black box” operation whose results are difficult to interpret and sometimes counter‐intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high‐level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.

We present an interactive framework for exploring space-time and form-function relationships in experimentally collected high-resolution biomechanical data sets. These data describe complex 3D motions (e.g. chewing, walking, flying) performed by animals and humans and captured via high-speed imaging technologies, such as biplane fluoroscopy. In analyzing these 3D biomechanical motions, interactive 3D visualizations are important, in particular, for supporting spatial analysis. However, as researchers in information visualization have pointed out, 2D visualizations can also be effective tools for multi-dimensional data analysis, especially for identifying trends over time. Our approach, therefore, combines techniques from both 3D and 2D visualizations. Specifically, it utilizes a multi-view visualization strategy including a small multiples view of motion sequences, a parallel coordinates view, and detailed 3D inspection views. The resulting framework follows an overview first, zoom and filter, then details-on-demand style of analysis, and it explicitly targets a limitation of current tools, namely, supporting analysis and comparison at the level of a collection of motions rather than sequential analysis of a single or small number of motions. Scientific motion collections appropriate for this style of analysis exist in clinical work in orthopedics and physical rehabilitation, in the study of functional morphology within evolutionary biology, and in other contexts. An application is described based on a collaboration with evolutionary biologists studying the mechanics of chewing motions in pigs. Interactive exploration of data describing a collection of more than one hundred experimentally captured pig chewing cycles is described.

Abstract Many previous approaches to detecting urban change from LIDAR point clouds interpolate the points into rasters, perform pixel‐based image processing to detect changes, and produce 2D images as output. We present a method of LIDAR change detection that maintains accuracy by only using the raw, irregularly spaced LIDAR points, and extracts relevant changes as individual 3D models. We then utilize these models, alongside existing GIS data, within an interactive application that allows the chronological exploration of the changes to an urban environment. A three‐tiered level‐of‐detail system maintains a scale‐appropriate, legible visual representation across the entire range of view scales, from individual changes such as buildings and trees, to groups of changes such as new residential developments, deforestation, and construction sites, and finally to larger regions such as neighborhoods and districts of a city that are emerging or undergoing revitalization. Tools are provided to assist the visual analysis by urban planners and historians through semantic categorization and filtering of the changes presented.

Abstract Recent increases in terrorist activity around the world have made analyzing and understanding such activities more critical than ever. With the help of organizations such as the National Center for the Study of Terrorism and Responses to Terrorism (START), we now have detailed historical information on each terrorist event around the world since 1970. However, due to the size and complexity of the data, identifying terrorists' patterns and trends has been difficult. To better enable investigators in understanding terrorist activities, we propose a visual analytical system that focuses on depicting one of the most fundamental concepts in investigative analysis, the five W's (who, what, where, when, and why). Views in our system are highly correlated, and each represents one of the W's. With this approach, an investigator can interactively explore terrorist activities efficiently and discover reasons of attacks (why) by identifying patterns temporally (when), geo‐spatially (where), between multiple terrorist groups (who), and across different methods or modes of attacks (what). By coupling a global perspective with the details gleaned from asking these five questions, the system allows analysts to think both tactically and strategically.

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.

Numerous systems have been developed to display large collections of data for urban contexts; however, most have focused on layering of single dimensions of data and manual calculations to understand relationships within the urban environment. Furthermore, these systems often limit the userâs perspectives on the data, thereby diminishing the userâs spatial understanding of the viewing region. In this paper, we introduce a highly interactive urban visualization tool that provides intuitive understanding of the urban data. Our system utilizes an aggregation method that combines buildings and city blocks into legible clusters, thus providing continuous levels of abstraction while preserving the userâs mental model of the city. In conjunction with a 3D view of the urban model, a separate but integrated information visualization view displays multiple disparate dimensions of the urban data, allowing the user to understand the urban environment both spatially and cognitively in one glance. For our evaluation, expert users from various backgrounds viewed a real city model with census data and confirmed that our system allowed them to gain more intuitive and deeper understanding of the urban model from different perspectives and levels of abstraction than existing commercial urban visualization systems.

Few existing visualization systems can handle large data sets with hundreds of dimensions, since high-dimensional data sets cause clutter on the display and large response time in interactive exploration. In this paper, we present a significantly improved multidimensional visualization approach named Value and Relation (VaR) display that allows users to effectively and efficiently explore large data sets with several hundred dimensions. In the VaR display, data values and dimension relationships are explicitly visualized in the same display by using dimension glyphs to explicitly represent values in dimensions and glyph layout to explicitly convey dimension relationships. In particular, pixel-oriented techniques and density-based scatterplots are used to create dimension glyphs to convey values. Multidimensional scaling, Jigsaw map hierarchy visualization techniques, and an animation metaphor named Rainfall are used to convey relationships among dimensions. A rich set of interaction tools has been provided to allow users to interactively detect patterns of interest in the VaR display. A prototype of the VaR display has been fully implemented. The case studies presented in this paper show how the prototype supports interactive exploration of data sets of several hundred dimensions. A user study evaluating the prototype is also reported in this paper.

As the content of everyday news reports is unpredictable, keyword based news search engine can't provide effective services to audiences because the audiences may not be able to figure out proper keywords to search. In this paper, a novel framework is proposed to help audiences browse and retrieve news video clips without the need of keywords. Interesting keyframes and keywords are automatically extracted from news video clips and visually represented according to their interestingness and informativeness measurement. A computational approach is also developed to quantify the interestingness measurement of video clips. The keyframes and keywords are carefully organized so that the audiences can find news stories of interest at first glance.

This paper describes a framework we have developed for the visual analysis of large-scale phylogeny hierarchies populated with the genomic data of various organisms. This framework allows the user to quickly browse the phylogeny hierarchy of organisms from the highest level down to the level of an individual genome for the desired organism of interest. Based on this framework, the user can initiate gene-finding and gene-matching analyses and view the resulting annotated coding potential graphs in the same multi-scale visualization framework, permitting correlative analysis and further investigation. This paper introduces our framework and describes the data structures and algorithms that support it.

This poster explores navigation, interaction and stereoscopic display in a multi-scale virtual space. When interaction, especially two-handed, direct manipulation, is combined with stereoscopic stationary displays, there are trade-offs that must be considered. This poster gives an overview of these design issues and briefly describes our current multi-scale, application for exploring volumetric weather in its geospatial context in a VR system. The general importance of recognizing and using defined areas of user focus and of semi-automatically bringing these areas into the optimal interaction volume of the display system is described.

Guest Editorial: Special Issue on Haptics, Virtual, and Augmented Reality

In this paper a new quadric-based view-dependent simplification scheme is presented. The scheme provides a method to connect mesh simplification controlled by a quadric error metric with a level-of-detail hierarchy that is accessed continuously and efficiently based on current view parameters. A variety of methods for determining the screen-space metric for the view calculation are implemented and evaluated, including an appearance-preserving method that has both geometry- and texture-preserving aspects. Results are presented and compared for a variety of models.

This paper describes an experimental investigation into the relationship of delay to user performance in 3D object placement. In the first experiment, 10 participants performed a placement task as delay and placement difficulty varied. Results were analyzed with statistical analyses of variance. Placement errors and times rose as delay and difficulty increased. An interaction indicated that controlling delay is particularly important when placement is already difficult. In vehicle control tasks, previewing forecasts required input, and is a useful way of compensating for delay. The second experiment examined the relationship of previewing to delay and difficulty in placement. With regard to delay and difficulty, the 15 participants replicated the first experiment's results. Previewing reduced placement times directly, and mitigated the effects of delay and difficulty. A three-way interaction indicated that when previewing is used, more delay can be tolerated even in difficult placement tasks.

This paper presents a new method for the automatic modeling of architecture. Building designs are derived using split grammars, a new type of parametric set grammar based on the concept of shape. The paper also introduces an attribute matching system and a separate control grammar, which offer the flexibility required to model buildings using a large variety of different styles and design ideas. Through the adaptive nature of the design grammar used, the created building designs can either be generic or adhere closely to a specified goal, depending on the amount of data available.

Novel speech and/or gesture interfaces are candidates for use in future mobile or ubiquitous applications. This paper describes an evaluation of various interfaces for visual navigation of a whole Earth 3D terrain model. A mouse driven interface, a speech interface, a gesture interface, and a multimodal speech and gesture interface were used to navigate to targets placed at various points on the Earth. This study measured each participant's recall of target identity, order, and location as a measure of cognitive load. Timing information as well as a variety of subjective measures including discomfort and user preference were taken. While the familiar and mature mouse interface scored best by most measures, the speech interface also performed well. The gesture and multimodal interface suffered from weaknesses in the gesture modality. Weaknesses in the speech and multimodal modalities are identified and areas for improvement are discussed.

This paper concerns stereoscopic virtual reality displays in which the head is tracked and the display is stationary, attached to a desk, tabletop or wall. These are called stereoscopic HTDs (head-tracked displays). Stereoscopic displays render two perspective views of a scene, each of which is seen by one eye of the user. Ideally, the user's natural visual system combines the stereo image pair into a single, 3D perceived image. Unfortunately, users often have difficulty fusing the stereo image pair. Researchers use a number of software techniques to reduce fusion problems. This paper geometrically examines and compares a number of these techniques and reaches the following conclusions: In interactive stereoscopic applications, the combination of view placement, scale, and either false eye separation or /spl alpha/-false eye separation can provide fusion control that is geometrically similar to image shifting and image scaling. However, in stereo HTDs, image shifting and image scaling also generate additional geometric artifacts that are not generated by the other methods. We anecdotally link some of these artifacts to exceeding the perceptual limitations of human vision. While formal perceptual studies are still needed, geometric analysis suggests that image shifting and image scaling may be less appropriate than the other methods for interactive, stereo HTDs.

As part of a process to identify potential simulator sickness issues with our NAVE (Non-expensive Automatic Virtual Environment), a new virtual environment display system developed at Georgia Tech, we have conducted a study to address the experience of simulator sickness and presence under different display and user role configurations. The NAVE has three 8 feet by 6 feet screens. The two side screens are positioned at 120 degree angles to the central screen to give a three-sided display area that is sixteen feet wide and approximately seven feet deep. This allows for two different field of view configurations, a one-screen set-up that provides a 60 degree FOV, and a three-screen set-up that provides a 180 degree FOV. Users are seated in front of the center screen and navigate with a joystick. The virtual environment used in this study could be displayed in the NAVE stereo-visually or in mono, providing us with a second experimental factor, display fidelity. Lastly, we were interested in determining whether or not the user's role in the environment would affect their experience of simulator sickness and presence as suggested by Stanney and Kennedy (1997). This provided us with our third factor user role, with its two levels, driver and passenger.

In this paper, we introduce a new style of visualization called Situational Visualization, in which the user of a robust, mobile visualization system uses mobile computing resources to enhance the experience and understanding of the surrounding world. Additionally, a Situational Visualization system allows the user to add to the visualization and any underlying simulation by inputting the user's observations of the phenomena of interest, thus improving the quality of visualization for the user and for any other users that may be connected to the same database. Situational Visualization allows many users to collaborate on a common set of data with real-time acquisition and insertion of data. In this paper, we present a Situational Visualization system we are developing called Mobile VGIS, and present two sample applications of Situational Visualization

The Perceptive Workbench enables a spontaneous, natural and unimpeded interface between the physical and virtual worlds. It uses vision-based methods for interaction that eliminate the need for wired input devices and wired tracking. Objects are recognized and tracked when placed on the display surface. Through the use of multiple light sources, the object's 3D shape can be captured and inserted into the virtual interface. This ability permits spontaneity since either preloaded objects or those objects selected on-the-spot by the user can become physical icons. Integrated into the same vision-based interface is the ability to identify 3D hand position, pointing direction and sweeping arm gestures. Such gestures can enhance selection, manipulation and navigation tasks. In this paper, the Perceptive Workbench is used for augmented reality gaming and terrain navigation applications, which demonstrate the utility and capability of the interface.

1.1: In discussion of OpenGL implementation the “[α]view = T-1 • α •T ” included an implicit scale factor. For clarity I replaced this with “[α]view = S-1•T-1 • α •T•S ” in order to make the scale factor’s necessity clearer. ACM COPYRIGHT NOTICE. Copyright © 1999 by the Association for Computing Machinery, Inc. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is

This paper presents results for real-time visualization of out-of-core collections of 3D objects. This is a significant extension of previous methods and shows the generality of hierarchical paging procedures applied both to global terrain and any objects that reside on it. Applied to buildings, the procedure shows the effectiveness of using a screen-based paging and display criterion within a hierarchical framework. The results demonstrate that the method is scalable since it is able to handle multiple collections of buildings (e.g., cities) placed around the earth with full interactivity and without extensive memory load. Further the method shows efficient handling of culling and is applicable to larger, extended collections of buildings. Finally, the method shows that levels of detail can be incorporated to provide improved detail management.

Navigation and interaction in virtual environments that use stereoscopic head-tracked displays and have very large data sets present several challenges beyond those encountered with smaller data sets and simpler displays. First, zooming by approaching or retreating from a target must be augmented by integrating scale as a seventh degree of freedom. Second, in order to maintain good stereoscopic imagery, the interface must: maintain stereo image pairs that the user perceives as a single 3D image, minimize loss of perceived depth since stereoscopic imagery cannot properly occlude the screen's frame, provide maximum depth information, and place objects at distances where they are best manipulated. Finally, the navigation interface must work when the environment is displayed at any scale. This paper addresses these problems for god's-eye-view or third person navigation of a specific large-scale virtual environment: a high-resolution terrain database covering an entire planet.

This paper considers how out-of-core visualization applies to terrain datasets, which are among the largest now presented for interactive visualization and can range to sizes of 20 GB and more. It is found that a combination of out-of-core visualization, which tends to focus on 3D data, and visual simulation, which places an emphasis on visual perception and real-time display of multiresolution data, results in interactive terrain visualization with significantly improved data access and quality of presentation. Further, the visual simulation approach provides qualities that are useful for general data, not just terrain.

Te authors present the Virtual Data Visualizer, a highly interactive, immersive environment for visualizing and analyzing data. VDV is a set of tools for exploratory data visualization that does not focus on just one type of application. It employs a data organization with data arranged hierarchically in classes that can be modified by the user within the virtual environment. The class structure is the basis for bindings or mappings between data variables and glyph elements, which the user can make, change, or remove. The binding operation also has a set of defaults so that the user can quickly display the data. The VDV requires a user interface that is fairly complicated for a virtual environment. They have taken the approach that a combination of more-or-less traditional menus and more direct means of icon manipulation will do the job. This work shows that a useful interface and set of tools can be built. Controls in VDV include a panel for controlling animation of the data and zooming in and out. Tools include a workbench for changing the glyphs and setting glyph/variable ranges and a boundary tool for defining new classes spatially.

We present an algorithm for real-time level of detail reduction and display of high-complexity polygonal surface data. The algorithm uses a compact and efficient regular grid representation, and employs a variable screen-space threshold to bound the maximum error of the projected image. A coarse level of simplification is performed to select discrete levels of detail for blocks of the surface mesh, followed by further simplification through repolygonalization in which individual mesh vertices are considered for removal. These steps compute and generate the appropriate level of detail dynamically in real-time, minimizing the number of rendered polygons and allowing for smooth changes in resolution across areas of the surface. The algorithm has been implemented for approximating and rendering digital terrain models and other height fields, and consistently performs at interactive frame rates with high image quality. 1 INTRODUCTION Modern graphics workstations allow the display of thousa...

Advances in computer graphics hardware and algorithms, visualization, and interactive techniques for analysis offer the components for a highly integrated, efficient real-time 3D Geographic Information System. We have developed "Virtual GIS", a system with truly immersive capability for navigating and understanding complex and dynamic terrain-based databases. The system provides the means for visualizing terrain models consisting of elevation and imagery data, along with GIS raster layers, protruding features, buildings, vehicles, and other objects. We have implemented window-based and virtual reality versions and in both cases provide a direct manipulation, visual interface for accessing the GIS data. Unique terrain data structures and algorithms allow rendering of large, high resolution datasets at interactive rates.