IEEE VIS Publication Dataset

In this paper we present a hardware-assisted rendering technique coupled with a compression scheme for the interactive visual exploration of time-varying scalar volume data. A palette-based decoding technique and an adaptive bit allocation scheme are developed to fully utilize the texturing capability of a commodity 3-D graphics card. Using a single PC equipped with a modest amount of memory, a texture capable graphics card, and an inexpensive disk array, we are able to render hundreds of time steps of regularly gridded volume data (up to 45 millions voxels each time step) at interactive rates, permitting the visual exploration of large scientific data sets in both the temporal and spatial domain.

We have developed a fast, perceptual method for selecting color scales for data visualization that takes advantage of our sensitivity to luminance variations in human faces. To do so, we conducted experiments in which we mapped various color scales onto the intensity values of a digitized photograph of a face and asked observers to rate each image. We found a very strong correlation between the perceived naturalness of the images and the degree to which the underlying color scales increased monotonically in luminance. Color scales that did not include a monotonically increasing luminance component produced no positive rating scores. Since color scales with monotonic luminance profiles are widely recommended for visualizing continuous scalar data, a purely visual technique for identifying such color scales could be very useful, especially in situations where color calibration is not integrated into the visualization environment, such as over the Internet.

Traditional methods for displaying weather products are generally two-dimensional (2D) plots or just text format. It is hard for forecasters to get the entire picture of the atmosphere using these methods. The problems apparent in 2D with comparing and correlating multiple layers are overcome simply by adding a dimension. This is important because pertinent features in the data sets may lie in multiple layers and span several time steps. However, simply using a three-dimensional (3D) approach is not enough. The capacity for analysis of small-scale, but important, features in 2D are lost when transitioning to 3D. We propose that 3D's advantages can be incorporated with 2D's small-scale analysis by using an immersive virtual environment. In this case study, we evaluate our current standing with the project: have we met our goals, and how should we proceed from this point? To evaluate our application, we invited meteorologists to use the application to explore a data set. Then we presented our goals and asked which ones had we met, from a meteorologist's perspective. The results qualitatively reflected that our application was effective and further research would be worthwhile.

Since the original paper of Lacroute and Levoy (1994), where the shear-warp factorization was also shown for perspective projections, a lot of work has been carried out using the shear-warp factorization with parallel projections. However, none of it has proved or improved the algorithm for the perspective projection. Also in Lacroute's Volpack library, the perspective shear-warp volume rendering algorithm is missing. This paper reports on an implementation of the perspective shear-warp algorithm, which includes enhancements for its application in immersive virtual environments. Furthermore, a mathematical proof for the correctness of the permutation of projection and warp is provided, so far a basic assumption of the shear-warp perspective projection.

The visualization of time-dependent flow is an important and challenging topic in scientific visualization. Its aim is to represent transport phenomena governed by time-dependent vector fields in an intuitively understandable way, using images and animations. Here we pick up the recently presented anisotropic diffusion method, expand and generalize it to allow a multiscale visualization of long-term, complex transport problems. Instead of streamline type patterns generated by the original method now streakline patterns are generated and advected. This process obeys a nonlinear transport diffusion equation with typically dominant transport. Starting from some noisy initial image, the diffusion actually generates and enhances patterns which are then transported in the direction of the flow field. Simultaneously the image is again sharpened in the direction orthogonal to the flow field. A careful adjustment of the models parameters is derived to balance diffusion and transport effects in a reasonable way. Properties of the method can be discussed for the continuous model, which is solved by an efficient upwind finite element discretization. As characteristic for the class of multiscale image processing methods, we can in advance select a suitable scale for representing the flow field.

Shape modeling is an integral part of many visualization problems. Recent advances in scanning technology and a number of surface reconstruction algorithms have opened up a new paradigm for modeling shapes from samples. Many of the problems currently faced in this modeling paradigm can be traced back to two anomalies in sampling, namely undersampling and oversampling. Boundaries, non-smoothness and small features create undersampling problems, whereas oversampling leads to too many triangles. We use Voronoi cell geometry as a unified guide to detect undersampling and oversampling. We apply these detections in surface reconstruction and model simplification. Guarantees of the algorithms can be proved. The authors show the success of the algorithms empirically on a number of interesting data sets.

We present several techniques for user-centric viewing of the virtual objects or datasets under haptic exploration and manipulation. Depending on the type of tasks performed by the user, our algorithms compute automatic placement of the user viewpoint to navigate through the scene, to display the near-optimal views, and to reposition the viewpoint for haptic visualization. This is accomplished by conjecturing the user's intent based on the user's actions, the object geometry, and intra- and inter-object occlusion relationships. These algorithms have been implemented and interfaced with both a 3-DOF and a 6-DOF PHANToM arms. We demonstrate their application on haptic exploration and visualization of a complex structure, as well as multiresolution modeling and 3D painting with a haptic interface.

We present a new technique for visualizing surfaces from 3D ultrasound data. 3D ultrasound datasets are typically fuzzy, contain a substantial amount of noise and speckle, and suffer from several other problems that make extraction of continuous and smooth surfaces extremely difficult. We propose a novel opacity classification algorithm for 3D ultrasound datasets, based on the variational principle. More specifically, we compute a volumetric opacity function that optimally satisfies a set of simultaneous requirements. One requirement makes the function attain nonzero values only in the vicinity of a user-specified value, resulting in soft shells of finite, approximately constant thickness around isosurfaces in the volume. Other requirements are designed to make the function smoother and less sensitive to noise and speckle. The computed opacity function lends itself well to explicit geometric surface extraction, as well as to direct volume rendering at interactive rates. We also describe a new splatting algorithm that is particularly well suited for displaying soft opacity shells. Several examples and comparisons are included to illustrate our approach and demonstrate its effectiveness on real 3D ultrasound datasets.

The Temporal Bone Dissection Simulator is an ongoing research project for the construction of a synthetic environment suitable for virtual dissection of human temporal bone and related anatomy. Funded by the National Institute on Deafness and Other Communication Disorders (NIDCD), the primary goal of this project is to provide a safe, robust, and cost-effective virtual environment for learning the anatomy and surgical procedures associated with the temporal bone. Direct volume visualization has been indispensable for the necessary level of realism and interactivity that is vital to the success of this project. This work is being conducted by the Ohio Supercomputer Center in conjunction with the Department of Otolaryngology at the Ohio State University, and NIDCD.

We describe a pipeline of image processing steps for deriving symbolic models of vascular structures from radiological data which reflect the branching pattern and diameter of vessels. For the visualization of these symbolic models, concatenated truncated cones are smoothly blended at branching points. We put emphasis on the quality of the visualizations which is achieved by anti-aliasing operations in different stages of the visualization. The methods presented are referred to as HQVV (high quality vessel visualization). Scalable techniques are provided to explore vascular structures of different orders of magnitude. The hierarchy as well as the diameter of the branches of vascular systems are used to restrict visualizations to relevant subtrees and to emphasize parts of vascular systems. Our research is inspired by clear visualizations in textbooks and is targeted toward medical education and therapy planning. We describe the application of vessel visualization techniques for liver surgery planning. For this application it is crucial to recognize the morphology and branching pattern of vascular systems as well as the basic spatial relations between vessels and other anatomic structures.

We present an elegant and simple to implement framework for performing out-of-core visualization and view-dependent refinement of large terrain surfaces. Contrary to the trend of increasingly elaborate algorithms for large-scale terrain visualization, our algorithms and data structures have been designed with the primary goal of simplicity and efficiency of implementation. Our approach to managing large terrain data also departs from more conventional strategies based on data tiling. Rather than emphasizing how to segment and efficiently bring data in and out of memory, we focus on the manner in which the data is laid out to achieve good memory coherency for data accesses made in a top-down (coarse-to-fine) refinement of the terrain. We present and compare the results of using several different data indexing schemes, and propose a simple to compute index that yields substantial improvements in locality and speed over more commonly used data layouts. Our second contribution is a new and simple, yet easy to generalize method for view-dependent refinement. Similar to several published methods in this area, we use longest edge bisection in a top-down traversal of the mesh hierarchy to produce a continuous surface with subdivision connectivity. In tandem with the refinement, we perform view frustum culling and triangle stripping. These three components are done together in a single pass over the mesh. We show how this framework supports virtually any error metric, while still being highly memory and compute efficient.

Remote experience of sporting events has thus far been limited mostly to watching video and the scores and statistics associated with the sport. However, a fast-developing trend is the use of visualization techniques to give new insights into performance, style, and strategy of the players. Automated techniques can extract accurate information from video about player performance that not even the most skilled observer is able to discern. When presented as static images or as a three-dimensional virtual replay, this information makes viewing a game an entirely new and exciting experience.This paper presents one such sports visualization system called LucentVision, which has been developed for the sport of tennis. LucentVision uses real-time video analysis to obtain motion trajectories of players and the ball, and offers a rich set of visualization options based on this trajectory data. The system has been used extensively in the broadcast of international tennis tournaments, both on television and the Internet.

Maps of biophysical and geophysical variables using Earth Observing System (EOS) satellite image data are an important component of Earth science. These maps have a single value derived at every grid cell and standard techniques are used to visualize them. Current tools fall short, however, when it is necessary to describe a distribution of values at each grid cell. Distributions may represent a frequency of occurrence over time, frequency of occurrence from multiple runs of an ensemble forecast or possible values from an uncertainty model. We identify these "distribution data sets" and present a case study to visualize such 2D distributions. Distribution data sets are different from multivariate data sets in the sense that the values are for a single variable instead of multiple variables. Data for this case study consists of multiple realizations of percent forest cover, generated using a geostatistical technique that combines ground measurements and satellite imagery to model uncertainty about forest cover. We present two general approaches for analyzing and visualizing such data sets. The first is a pixel-wise analysis of the probability density functions for the 2D image while the second is an analysis of features identified within the image. Such pixel-wise and feature-wise views will give Earth scientists a more complete understanding of distribution data sets. See www.cse.ucsc.edu/research/avis/nasa is for additional information.

This paper presents a method concerning the volume rendering of fine details, such as blood vessels and nerves, from medical data. The realistic and efficient visualization of such structures is often of great medical interest, and conventional rendering techniques do not always deal with them adequately. Our method uses preprocessing to reconstruct fine details that are difficult to segment and label. It detects the presence of fine geometrical structures, such as cracks or cylinders that suggest the existence of, for example, blood vessels or nerves; the subsequent volume rendering then displays fine geometrical objects that lie on a surface. The method can also show structures within the volume, using a special "integration sampling" scheme to portray reconstructed volume texture, such as that exhibited by muscle fibers. By combining the surface structure and volume texture in the rendering, realistic results can be produced; examples are provided.

We present a new wavelet compression and multiresolution modeling approach for sets of contours (level sets). In contrast to previous wavelet schemes, our algorithm creates a parametrization of a scalar field induced by its contours and compactly stores this parametrization rather than function values sampled on a regular grid. Our representation is based on hierarchical polygon meshes with subdivision connectivity whose vertices are transformed into wavelet coefficients. From this sparse set of coefficients, every set of contours can be efficiently reconstructed at multiple levels of resolution. When applying lossy compression, introducing high quantization errors, our method preserves contour topology, in contrast to compression methods applied to the corresponding field function. We provide numerical results for scalar fields defined on planar domains. Our approach generalizes to volumetric domains, time-varying contours, and level sets of vector fields.

This case study describes the process of fusing the data from several wind tunnel experiments into a single coherent visualization. Each experiment was conducted independently and was designed to explore different flow features around airplane landing gear. In the past, it would have been very difficult to correlate results from the different experiments. However, with a single 3-D visualization representing the fusion of the three experiments, significant insight into the composite flowfield was observed that would have been extremely difficult to obtain by studying its component parts. The results are even more compelling when viewed in an immersive environment.

The paper describes major concepts of a scalable information visualization framework. We assume that the exploration of heterogeneous information spaces at arbitrary levels of detail requires a suitable preprocessing of information quantities, the combination of different graphical interfaces and the illustration of the frame of reference of given information sets. The innovative features of our system include: dynamic hierarchy computation and user controlled refinement of those hierarchies for preprocessing unstructured information spaces; a new Focus+Context technique for visualizing complex hierarchy graphs; a new paradigm for visualizing information structures within their frame of reference; and a new graphical interface that utilizes textual similarities to arrange objects of high dimensional information space in 3-dimensional visualization space

In previous work, researchers have attempted to construct taxonomies of information visualization techniques by examining the data domains that are compatible with these techniques. This is useful because implementers can quickly identify various techniques that can be applied to their domain of interest. However, these taxonomies do not help the implementers understand how to apply and implement these techniques. The author extends and proposes a new way to taxonomize information visualization techniques by using the Data State Model (E.H. Chi and J.T. Reidl, 1998). In fact, as the taxonomic analysis in the paper will show, many of the techniques share similar operating steps that can easily be reused. The paper shows that the Data State Model not only helps researchers understand the space of design, but also helps implementers understand how information visualization techniques can be applied more broadly

We describe a prototype same-time/different-place collaborative geovisualization environment. We outline an approach to understanding use and usability and present results of interviews with domain experts about the ways in which collaborative visualization might enable groups to work at a distance. One goal for our research is to design an effective and flexible system that can support group work on environmental science research mediated through dynamic geovisualization displays. We are addressing this goal using a four-step human-centered system design process, modeled on that proposed by (Gabbard et al., 1999) for development and evaluation of virtual environments. The steps they delineate are: user task analysis; expert guideline-based evaluation; formative user-centered evaluation; and summative comparative evaluation

Drawing on ethnographic studies of (landscape) architects at work, this paper presents a human-centered approach to information visualization. A 3D collaborative electronic workspace allows people to configure, save and browse arrangements of heterogeneous work materials. Spatial arrangements and links are created and maintained as an integral part of ongoing work with `live' documents and objects. The result is an extension of the physical information space of the architects' studio that utilizes the potential of electronic data storage, visualization and network technologies to support work with information in context