In the last years there is a growing demand of multimodal medical rendering systems able to visualize simultaneously data coming from different sources. This paper addresses the Direct Volume Rendering (DVR) of aligned multimodal data in medical applications. Specifically, it proposes a hierarchical representation of the multimodal data set based on the construction of a Fusion Decision Tree (FDT) that, together with a run-length encoding of the non-empty data, provides means of efficiently accessing to the data. Three different implementations of these structures are proposed. The simulations results show that the traversal of the data is fast and that the method is suitable when interactive modifications of the fusion parameters are required.

Ultrasound is a non-invasive, cheap and portable imaging system available in most hospitals. It would be beneficial to surgeons to use an ultrasound transducer as a real time intraoperative positional pointer to previously acquired surgical planning images. We have developed a system to link live intraoperative ultrasound images with pre-operative surgical planning images utilizing a 3-dimensional image index block, a multimedia database, and a graphical wire-frame model. An index block is created using a set of planning images, from which images in any plane can be interpolated. The new images created are ordered in a database using bandpassed spatial feature detection, from which a smaller set of images are selected by feature presence and position for faster matching to a live image. Preliminary testing of the system has evaluated the image matching accuracy and tolerances, and results show that in over 90% cases a 'good' match can be found from a database containing approximately 3000 images in only 30 seconds, with significant correlations found between the coordinates of the live and best match images, indicating the potential for live ultrasound to act as a near-real time positional pointer to planning data.

This paper introduces a set of visualization tools that aim to support social navigation, the evaluation and optimization of three-dimensional virtual worlds and the study of their evolving communities. Previous work (Borner & Lin, 2001; Borner et al., 2002) demonstrated how this toolset can be used to analyze data acquired during a information treasure hunt. This paper extends and applies this tool set to visualize an entire virtual Conference on the topic Virtual Learning in Three Dimensions (VLearn3D) that was held in December 2002.

This paper presents a novel exploratory information visualization technique that allows users to analyze time-varying characteristics of large datasets within immersive virtual reality environments. This metaphor represents data objects as particles, coined infoticles, which are placed inside a three-dimensional scene. Forces correspond to specific data value conditions and influence matching infoticles according to the rules of Newtonian mechanics. In addition, infoticles are driven by a set of local behavior rules that react upon successive data updates, hereby generating distinct emergent motion typologies which are visually interpretable by users. These data patterns can be detected dynamically by observing the spatial transformations of infoticle streams, or statically, by interpreting the shapes of individual pathlines. This visualization method exploits the qualities of immersive virtual reality technology as it combines the characteristics of behavior generation and motion perception with the concepts of spatial awareness and stereoscopic vision. Infoticles are useful in visualizing time-varying characteristics of large, dynamic datasets because of their cognitively distinguishable and interpretative animation properties. The generation and evolution of infoticle patterns are based upon empirically defined grammatical rules. These visualization principles are demonstrated using the access logs of an internal knowledge document management website of a global consultancy company.

We present a system for the visualization of computing literature with an emphasis on collaboration patterns, interactions between related research specialties and the evolution of these characteristics through time. Our computing literature visualization system, has four major components: A mapping of bibliographical data to relational schema coupled with an RDBMS to store the relational data, an interactive GUI that allows queries and the dynamic construction of graphs, a temporal graph layout algorithm, and an interactive visualization tool. We use a novel technique for visualization of large graphs that evolve through time. Given a dynamic graph, the layout algorithm produces two-dimensional representations of each timeslice, while preserving the mental map of the graph from one slice to the next. A combined view, with all the timeslices can also be viewed and explored. For our analysis we use data from the Association of Computing Machinery's Digital Library of Scientific Literature which contains more than one hundred thousand research papers and authors. Our system can be found online at http://tgrip.cs.arizona.edu.

We present an interactive visualization technique for spatial probability density function data. These are datasets that represent a spatial collection of random variables, and contain a number of possible outcomes for each random variable. It is impractical to visualize all the information at each spatial location as it will quickly lead to a cluttered image. We advocate the use of hierarchical clustering as a means of summarizing the information, and also as a tool to bring out meaningful spatial structures in the datasets. For clustering, we discuss a distance function which preserves the spatial correlation present in these datasets. To create an informative visualization of the clusters, we introduce a scheme of colors and patterns to represent statistical properties of the clusters.

Data visualization techniques are penetrating in various technological areas. In the field of multimedia such as information search and retrieval in multimedia archives, or digital media production and post-production, data visualization methodologies based on large graphs give an exciting alternative to conventional storyboard visualization. In this paper we develop a new approach to visualization of multimedia (video) documents based both on large graph clustering and preliminary video segmenting and indexing.

This paper advocates the study and use of visualization design patterns to improve development productivity and usage effectiveness in dynamic, analytical data visualization. Nine visualization design patterns are presented formally using the current de facto pattern description language. Organized in three categories (data, structural, and behavioral), these patterns summarize many common practices and techniques used in the process of dynamic, analytical data visualization. A relationship diagram is also introduced to illustrate the common relationships and uses of the patterns. Driven by the study of design patterns, a simple, yet powerful, architecture design for a dynamic, analytical data visualization library is proposed. The fundamental characteristics of the design are component-based, data centric, and layout-aided.

We introduce a multi-layered image cache system that is designed to work with a pool of rendering engines to facilitate an interactive, frameless, asynchronous rendering environment. Our system decouples the rendering from the display of imagery. Therefore, it decouples render frequency and resolution from display frequency and resolution, and allows asynchronous transmission of imagery instead of the compute--send cycle of standard parallel systems. It also allows local, incremental refinement of imagery without requiring all imagery to be re-rendered. Images are placed in fixed position in camera (vs. world) space to eliminate occlusion artifacts. Display quality is improved by increasing the number of images. Interactivity is improved by decreasing the number of images.

We present a computer-assisted framework for visualizing and analyzing old and damaged manuscripts. Our framework provides tools that help integrate and visualize 2D and 3D data acquired from the manuscripts. Our framework has been designed by considering comments, feedback, and discussions with scholars and conservators who study and preserve these materials.

The appearance of the sky has a fundamental effect on the way human beings perceive an environment. This paper presents a method to compute synthetic high-dynamic-range fisheye images from weather parameter data sets. These images can then be used in global-illumination systems (e. g. Radiance) to define the lighting conditions at an arbitrary weather state. Applications of this technology can be found in flight simulators and in architectural visualization. The method combines artificial neural networks and principal component analysis to associate the appearance of the sky with the state of a weather parameter vector. A model is trained with examples of sky images and weather data from a period of seven months. This model is then used to generate artificial sky images corresponding to a specific weather parameter vector. This is a novel method which contrary to many previous methods is able to synthesize a sky image which varies with the current weather state. The results show that, although it is not possible to represent the cloud details, it is possible to distinguish between different weather states.

Basic bar charts have been commonly available, but they only show highly aggregated data. Finding the valuable information hidden in the data is essential to the success of business. We describe a new visualization technique called pixel bar charts, which are derived from regular bar charts. The basic idea of a pixel bar chart is to present all data values directly instead of aggregating them into a few data values. Pixel bar charts provide data distribution and exceptions besides aggregated data. The approach is to represent each data item (e.g. a business transaction) by a single pixel in the bar chart. The attribute of each data item is encoded into the pixel color and can be accessed and drilled down to the detail information as needed. Different color mappings are used to represent multiple attributes. This technique has been prototyped in three business service applications-Business Operation Analysis, Sales Analysis, and Service Level Agreement Analysis at Hewlett Packard Laboratories. Our applications show the wide applicability and usefulness of this new idea.

This paper describes information technology being developed to improve the quality, sophistication, accessibility, and pedagogical simplicity of ecological network data, analysis, and visualization. We present designs for a WWW demonstration/prototype web site that provides database, analysis, and visualization tools for research and education related to food web research. Our early experience with a prototype 3D ecological network visualization guides our design of a more flexible architecture design. 3D visualization algorithms include variable node and link sizes, placements according to node connectivity and tropic levels, and visualization of other node and link properties in food web data. The flexible architecture includes an XML application design, FoodWebML, and pipelining of computational components. Based on users’ choices of data and visualization options, the WWW prototype site will connect to an XML database (Xindice) and return the visualization in VRML format for browsing and further interactions.

Dynamic flow volume rendering of three-dimensional vector fields offers better insights into the continuum and dynamics of the data field under investigation. Consumer graphics cards have seen a rapid explosion of performance and capabilities over the past few years. This paper explores the development of the Textured Splats algorithm for direct flow volume rendering of vector fields, that utilizes this new hardware. This paper presents the technique using the new hardware features like vertex programs, OpenGL multi-textures and register combiner extensions to implement fast dynamic flow volume rendering on a PC equipped with an NVIDIA GeForce4 display card. Several anisotropic textured splats are investigated to implement flow volume rendering.

This paper proposes a new interactive flow visualization environment featured with an eye gaze interface. Gaze information is used to automatically adapt the display of flow field to the interest of users. We propose to use focus-plus-context techniques for effectively visualizing the local details together with the global context of large complex 2D flow fields in a gaze-directed way. Gaze-based interaction is also supported. Tracers can be automatically inserted at user’s gaze points. Since there are no necessity of using other input devices for interacting with the system except for observing the flow using their eyes, users are allowed to concentrate on the observing tasks and interact with the system in an easier, faster and more natural way.

Several authors have developed automated parameterized visualization generation systems14,15,16. All generate classic visualizations or combinations of such visualizations. A vector space model of visualization was proposed by Hoffman18, leading to the development of new visualizations and the concept of interpolating visualizations. These new visualizations provide alternative representations and insights into data and have been applied successfully in numerous data analysis problems including gene expression, drug discovery, clinical trials, toxicogenomics, and medical informatics23. In this paper we elevate this vector space model to include analytic visualizations, ones with tightly coupled analysis, such as Self-Organizing Maps (SOMs) and Multi-Dimensional Scaling (MDS). We describe our new model and provide an example interpolation of a SOM and a scatterplot with a simple data set (the Fisher Iris data) and a more complex and larger one (microarray gene expression data).

In this paper, we examine a modification to our three-dimensional point cloud reconstruction method, Gamma Shapes. Gamma Shapes is an extension to Alpha Shapes with the advantage that the Gamma Shape method allows the automatic selection of local scaling factors. This presentation examines scaling methods based on simple approximations of the point set’s medial axis.

A peer-to-peer collaborative visualization system has been built that can support both traditional displays and 3D virtual reality hardware. The software is built around Sun’s Java3D graphics and JXTA peer-to-peer networking APIs, allowing two users to load VRML geometry files and manipulate their contents. Although this software takes advantage of VR hardware, it may be used between any two Java supporting peers. Finally, because no dedicated server is required, collaborative visualizations across the web become easier to initiate and more spontaneous.

The field of Information Visualization is concerned with improving how users perceive, understand, and interact with visual representations of abstract information. Immersive Virtual Environments (VEs) excel at a greater comprehension of spatial information. This project addresses the intersection of these two fields known as Information-Rich Virtual Environments (IRVEs) where perceptually realistic information, such as models and scenes, are enhanced with abstract information, such as text, numeric data, hyperlinks, or multimedia resources. IRVEs present a number of important design challenges including the management, coordination, and display of interrelated perceptual and abstract information. We describe a set of design issues for this type of integrated visualization and demonstrate a coordinated, multiple-views approach to support 2D and 3D visualization interactions such as overview, navigation, details-on-demand, and brushing-and-linking. In the CAVE, spatial information in a VE is interactively linked to embedded visualizations of related abstract information. Software architecture issues are discussed with details of our implementation applied to the domain of chemical information visualization. Lastly, we subject our system to an informal usability evaluation and identify usability issues with interaction and navigation that guides future work in these environments.

In this study, we present a geovisualization tool using Alpha-shapes to visualize class clusters in a remotely sensed image classification. An Alpha-shape is an accurate representation of the shape of a cluster of points in a 2D or 3D feature space. Traditionally, spheres and ellipsoids are used to represent class clusters in a classification. These shapes, however, are rough approximations of irregular shaped class clusters. In remote sensing classification we often have to deal with these irregular clusters (e.g. concavities, pockets and voids) and Alpha-shapes will improve visualization of these classes. We argue that Alpha-shapes will also improve insight into a classification process, and related uncertainty. Uncertainty can arise from ambiguity in the attribution of class labels to pixels. This ambiguity is often caused by overlapping classes. Visualization is helpful in communicating this ambiguity as Alpha-shapes clearly show where classes overlap. In this study, we also propose and implement a novel classification algorithm based on Alpha-shapes. Most classification algorithms cannot cope with irregular and concave cluster shapes in feature space. We apply our algorithm on a Landsat 7 image scene of a study area in Southern France. We show that good classification results can be obtained with Alpha-shapes.

Interactive visual representations complement traditional statistical and machine learning techniques for data analysis, allowing users to play a more active role in a knowledge discovery process and making the whole process more understandable. Though visual representations are applicable to several stages of the knowledge discovery process, a common use of visualization is in the initial stages to explore and organize a sometimes unknown and complex data set. In this context, the integrated and coordinated-that is, user actions should be capable of affecting multiple visualizations when desired-use of multiple graphical representations allows data to be observed from several perspectives and offers richer information than isolated representations. In this paper we propose an underlying model for an extensible and adaptable environment that allows independently developed visualization components to be gradually integrated into a user configured knowledge discovery application. Because a major requirement when using multiple visual techniques is the ability to link amongst them, so that user actions executed on a representation propagate to others if desired, the model also allows runtime configuration of coordinated user actions over different visual representations. We illustrate how this environment is being used to assist data exploration and organization in a climate classification problem.

The increasing rate of growth in size of currently available datasets is a well known issue. The possibility of developing fast and easy to implement frameworks able to visualize at least part of a tera-sized volume is a challenging task. Several techniques have been proposed in recent years ranging from simplification to wavelet analysis. Subdivision methods have been one of the most successful techniques applied to the multi-resolution representation and visualization of surface meshes. Extensions of these techniques to the volumetric case presents positive effects and major challenges mainly concerning the generalization of the combinatorial structure of the refinement procedure and the analysis of the smoothness of the limit mesh. In this paper we address mainly the first part of the problem, presenting a framework that exploits a subdivision scheme suitable for extension to 3D and higher dimensional meshes. We introduce a technique that combines the flexibility of a progressive multi-resolution representation with the advantage of a recursive subdivision scheme. The main contributions of the paper are: (a) a progressive algorithm that builds a multi-resolution surface by successive refinements so that a consistent representation of the output is always available (b) a multi-resolution representation where any adaptively selected level of detail is guaranteed to be consistently embedded in 3D space (no self-intersections).

Davis (Data Viewing System) is a general-purpose data viewer designed for the simultaneous display of a large number of dynamic data sets. Davis was inspired by the need to explore computational models of the cerebral cortex. These systems are distinguished by complex dynamic elements interconnected in irregular patterns. Neuroscientists study the detailed behavior of individual elements and how these elements interact to achieve cortical function. This paper describes Davis and its use in cortical visualization. Davis is written in Java and can be run from a browser or as a standalone application. Users must provide an XML description of their data, which Davis uses for its menus, browsing and visualization. Davis visualizations can be applied to any collection of space-time data sets, and the Davis infrastructure allows visualizations to be added easily. Davis handles the synchronization of different visualizations and encapsulates different threading policies.

This paper discusses issues with the limited precision of hardware texture-based volume visualization. We will describe the compositing OVER operator and how fixed-point arithmetic affects it. We propose two techniques to improve the precision of fixed-point compositing and the accuracy of hardware-based volume visualization. The first technique is to perform dithering of color and alpha values. The sedond technique we call exponent-factoring, and captures significanly more numeric resolution than dithering, but can only produce monochromatic images.

Object-order volume rendering algorithms play important part in many visualization applications for their excellent performances. Though many volume rendering algorithms have been proposed during the past two decades, most of them are image-order algorithms. Splatting, one of the classical object-order algorithms, suffers from several kinds of aliasing artifacts for inaccuracy reasons. A much accurate object-order volume rendering algorithm is presented in this paper. By defining a set of data structures to serve as two step reconstruction lookup tables, together with using a simple voxel traversal and resample strategy, the new algorithm can not only get rid of inaccuracy of traditional splatting, but also have the features including high cache hit rate, easy to implement of parallelism and high speedup from pre-processing.

This paper describes the design and construction of a new visualization system for collections of heterogeneous information for intelligence analysis. The system has several novel features that taken together provide a highly modular and reusable framework for creating linked visual metaphors. The system leverages modern web technologies such as XML DOM and Schemas to create an expressive and powerful system. Examples of this are the ability to use the structure of the information being visualized (as expressed in an XML schema) to directly generate the object oriented code for manipulating that information, and the use of static and dynamic binding facilities for creating mappings between internal information items and visualization components. The resulting system blurs the distinction between information visualization and the World Wide Web. It is very modular and flexible, and supports rapid iteration and refinement of input data sources as well as interoperability with other information schemas. New linked visual metaphors can easily be added to the framework as required.

Data sets of up to 3000 journal abstracts from MEDLINE literature on the keyword combination `MAPK pathway' and `human' are visualized and analyzed for mitogen-activated protein kinase (MAPK) pathways. We have tightly coupled exploratory visualization with information extraction for interactive navigation through scattered information sources, in search of useful facts on MAPK by frequency-based filtering and amplification. Unlike direct database visualization that operates on curated data sets, literature visualization has the advantages of manipulating data sets of a massive scale with a lot less manpower and effectively responding to the fast cycles of the developments in the field.

Modern imaging and simulation techniques have enhanced system-level understanding of neural function. In this article, we present an application of interactive visualization to understanding neuronal dynamics causing locomotion of a single hip joint, based on pattern generator output of the spinal cord. Our earlier work visualized cell-level responses of multiple neuronal populations. However, the spatial relationships were abstract, making communication with colleagues difficult. We propose two approaches to overcome this: (1) building a 3D anatomical model of the spinal cord with neurons distributed inside, animated by the simulation and (2) adding limb movements predicted by neuronal activity. The new system was tested using a cat walking central pattern generator driving a pair of opposed spinal motoneuron pools. Output of opposing motoneuron pools was combined into a single metric, called "Net Neural Drive", which generated angular limb movement in proportion to its magnitude. Net neural drive constitutes a new description of limb movement control. The combination of spatial and temporal information in the visualizations elegantly conveys the neural activity of the output elements (motoneurons), as well as the resulting movement. The new system encompasses five biological levels of organization from ion channels to observed behavior. The system is easily scalable, and provides an efficient interactive platform for rapid hypothesis testing.

Satellite images are important sources of information for meteorologists to predict rapid weather changes, for example storms, now and in the near-future (Nowcasting). It is not possible to use traditional numerical weather forecasts for this purpose since these are computed with a time-lag of several hours. This means that the most recent weather changes are not taken into account. This paper presents a method to compute synthetic satellite images from simulated forecast files. The cloud information in numerical forecast data sets is of much more interest if it can be visualized with a well-known representation like the satellite image. The proposed method uses artificial neural network technology to construct a model which is trained with data from numerical forecasts and classified satellite data captured at the same points in time. The cloud cover parameters in the forecast data set are tied to the cloud classification in the satellite image using a point-to-point representation. The results show that this is a useful method to compute synthetic satellite images. The level of detail in the resulting images is lower than in a real satellite image, but detailed enough to provide information about the principal features of the cloud cover.

This paper describes the results of analysis and visualization of Animal Behavior research papers pulbished in major journals. Analysis is carried out to obtain associations among the citation records for the domain. The primary goal is to observe the content coverage and the prominent research areas and describe the research activities taking place over the past decade. The methodologies used to study the domain provide a potential platform that can be extended to cover the entire publication history of the domain. The fact that the field of Animal Behavior has remained uncharted to a great extent provides immense motivation for the study.

A new general-purpose technique for the visualization of time-dependent Symmetric positive definite tensor fields of rank two is described. It is based on a splatting technique that is built from tiny transparent glyph primitives which are capable of incorporating the full directional information content of a tensor. The result is an information-rich image that allows to read off the preferred directions in a tensor field. It is useful for analyzing slices or volumes of a three-dimensional tensor field and can be overlayed with standard volume rendering or color mapping. The application of the rendering technique is demonstrated on numerically simulated general relativistic data and a measured diffusion tensor field of a human brain.