IEEE VIS Publication Dataset

This paper presents a shading model for volumetric data which enhances the perception of surfaces within the volume. The model incorporates uniform diffuse illumination, which arrives equally from all directions at each surface point in the volume. This illumination is attenuated by occlusions in the local vicinity of the surface point, resulting in shadows in depressions and crevices. Experiments by other authors have shown that perception of a surface is superior under uniform diffuse lighting, compared to illumination from point source lighting.

Video data, generated by the entertainment industry, security and traffic cameras, video conferencing systems, video emails, and so on, is perhaps most time-consuming to process by human beings. In this paper, we present a novel methodology for "summarizing" video sequences using volume visualization techniques. We outline a system pipeline for capturing videos, extracting features, volume rendering video and feature data, and creating video visualization. We discuss a collection of image comparison metrics, including the linear dependence detector, for constructing "relative" and "absolute" difference volumes that represent the magnitude of variation between video frames. We describe the use of a few volume visualization techniques, including volume scene graphs and spatial transfer functions, for creating video visualization. In particular, we present a stream-based technique for processing and directly rendering video data in real time. With the aid of several examples, we demonstrate the effectiveness of using video visualization to convey meaningful information contained in video sequences.

In this paper we propose a new method for the creation of normal maps for recovering the detail on simplified meshes and a set of objective techniques to metrically evaluate the quality of different recovering techniques. The proposed techniques, that automatically produces a normal-map texture for a simple 3D model that "imitates" the high frequency detail originally present in a second, much higher resolution one, is based on the computation of per-texel visibility and self-occlusion information. This information is used to define a point-to-point correspondence between simplified and hires meshes. Moreover, we introduce a number of criteria for measuring the quality (visual or otherwise) of a given mapping method, and provide efficient algorithms to implement them. Lastly, we apply them to rate different mapping methods, including the widely used ones and the new one proposed here.

Visibility culling has the potential to accelerate large data visualization in significant ways. Unfortunately, existing algorithms do not scale well when parallelized, and require full re-computation whenever the opacity transfer function is modified. To address these issues, we have designed a Plenoptic Opacity Function (POF) scheme to encode the view-dependent opacity of a volume block. POFs are computed off-line during a pre-processing stage, only once for each block. We show that using POFs is (i) an efficient, conservative and effective way to encode the opacity variations of a volume block for a range of views, (ii) flexible for re-use by a family of opacity transfer functions without the need for additional off-line processing, and (iii) highly scalable for use in massively parallel implementations. Our results confirm the efficacy of POFs for visibility culling in large-scale parallel volume rendering; we can interactively render the Visible Woman dataset using software ray-casting on 32 processors, with interactive modification of the opacity transfer function on-the-fly.

The quality of volume visualization depends strongly on the quality of the underlying data. In virtual colonoscopy, CT data should be acquired at a low radiation dose that results in a low signal-to-noise ratio. Alternatively, MRI data is acquired without ionizing radiation, but suffers from noise and bias (global signal fluctuations). Current volume visualization techniques often do not produce good results with noisy or biased data. This paper describes methods for volume visualization that deal with these imperfections. The techniques are based on specially adapted edge detectors using first and second order derivative filters. The filtering is integrated into the visualization process. The first order derivative method results in good quality images but suffers from localization bias. The second order method has better surface localization, especially in highly curved areas. It guarantees minimal detail smoothing resulting in a better visualization of polyps.

The simulation of breaking of waves, the formation of thin spray sheets, and the entertainment of air around the next generation of naval surface combatants is an ongoing 3-year Department of Defense (DoD) Challenge Project. The goal of this project is a validated computation capability to model the full hydrodynamics around a surface combatant including all of the processes that affect mission and performance. Visualization of these large-scale simulations is paramount to understanding the complex physics involved. These simulations produce enormous data sets with both surface and volumetric qualities. Wave breaking, spray sheets, and air entertainment can be visualized using isosurfaces of scalar data. Visualization of quantities such as the vorticity field also provides insight into the dynamics of droplet and bubble formation. This paper documents the techniques used, results obtained, and lessons learned from the visualization of the hydrodynamics of naval vessels.

We develop a new approach to reconstruct non-discrete models from gridded volume samples. As a model, we use quadratic trivariate super splines on a uniform tetrahedral partition Δ. The approximating splines are determined in a natural and completely symmetric way by averaging local data samples, such that appropriate smoothness conditions are automatically satisfied. On each tetra-hedron of Δ , the quasi-interpolating spline is a polynomial of total degree two which provides several advantages including efficient computation, evaluation and visualization of the model. We apply Bernstein-Bezier techniques well-known in CAGD to compute and evaluate the trivariate spline and its gradient. With this approach the volume data can be visualized efficiently e.g., with isosurface ray-casting. Along an arbitrary ray the splines are univariate, piecewise quadratics and thus the exact intersection for a prescribed isovalue can be easily determined in an analytic and exact way. Our results confirm the efficiency of the quasi-interpolating method and demonstrate high visual quality for rendered isosurfaces.

We describe a visualization application intended for operational use in formulating business strategy in the customer service arena. The visualization capability provided in this application implicitly allows the user to better formulate the objective function for large optimization runs which act to minimize costs based on certain input parameters. Visualization is necessary because many of the inputs to the optimization runs are themselves strategic business decisions which are not pre-ordained. Both information visualization presentations and three-dimensional visualizations are included to help users better understand the cost/benefit tradeoffs of these strategic business decisions. Here, visualization explicitly provides value not possible algorithmically, as the perceived benefit of different combinations of service level does not have an a priori mathematical formulation. Thus, we take advantage of the fundamental power of visualization, bringing the user's intuition and pattern recognition skills into the solution, while simultaneously taking advantage of the strength of algorithmic approaches to quickly and accurately find an optimal solution to a well-defined problem.

This paper describes a set of techniques developed for the visualization of high-resolution volume data generated from industrial computed tomography for nondestructive testing (NDT) applications. Because the data are typically noisy and contain fine features, direct volume rendering methods do not always give us satisfactory results. We have coupled region growing techniques and a 2D histogram interface to facilitate volumetric feature extraction. The new interface allows the user to conveniently identify, separate or composite, and compare features in the data. To lower the cost of segmentation, we show how partial region growing results can suggest a reasonably good classification function for the rendering of the whole volume. The NDT applications that we work on demand visualization tasks including not only feature extraction and visual inspection, but also modeling and measurement of concealed structures in volumetric objects. An efficient filtering and modeling process for generating surface representation of extracted features is also introduced. Four CT data sets for preliminary NDT are used to demonstrate the effectiveness of the new visualization strategy that we have developed.

In this paper, we describe a set of 3D and 4D visualization tools and techniques for CORIE, a complex environmental observation and forecasting system (EOFS) for the Columbia River. The Columbia River, a complex and highly variable estuary, is the target of numerous cross-disciplinary ecosystem research projects and is at the heart of multiple sustainable development issues with long reaching implications for the Pacific Northwest. However, there has been until recently no comprehensive and objective system available for modeling this environment, and as a consequence, researchers and agencies have had inadequate tools for evaluating the effects of natural resource management decisions. CORIE was designed to address this gap and is a major step towards the vision of a scalable, multi-use, real-time EOFS. Although CORIE already had a rich set of visualization tools, most of them produced 2D visualizations and did not allow for interactive visualization. Our work adds advanced interactive 3D tools to CORIE, which can be used for further inspection of the simulated and measured data.

Weather visualization is a difficult problem because it comprises volumetric multi-field data and traditional surface-based approaches obscure details of the complex three-dimensional structure of cloud dynamics. Therefore, visually accurate volumetric multi-field visualization of storm scale and cloud scale data is needed to effectively and efficiently communicate vital information to weather forecasters, improving storm forecasting, atmospheric dynamics models, and weather spotter training. We have developed a new approach to multi-field visualization that uses field specific, physically-based opacity, transmission, and lighting calculations per-field for the accurate visualization of storm and cloud scale weather data. Our approach extends traditional transfer function approaches to multi-field data and to volumetric illumination and scattering.

Tracking and visualizing local features from a time-varying volumetric data allows the user to focus on selected regions of interest, both in space and time, which can lead to a better understanding of the underlying dynamics. In this paper, we present an efficient algorithm to track time-varying isosurfaces and interval volumes using isosurfacing in higher dimensions. Instead of extracting the data features such as isosurfaces or interval volumes separately from multiple time steps and computing the spatial correspondence between those features, our algorithm extracts the correspondence directly from the higher dimensional geometry and thus can more efficiently follow the user selected local features in time. In addition, by analyzing the resulting higher dimensional geometry, it becomes easier to detect important topological events and the corresponding critical time steps for the selected features. With our algorithm, the user can interact with the underlying time-varying data more easily. The computation cost for performing time-varying volume tracking is also minimized.

We introduce a method for the animation of fire propagation and the burning consumption of objects represented as volumetric data sets. Our method uses a volumetric fire propagation model based on an enhanced distance field. It can simulate the spreading of multiple fire fronts over a specified isosurface without actually having to create that isosurface. The distance field is generated from a specific shell volume that rapidly creates narrow spatial bands around the virtual surface of any given isovalue. The complete distance field is then obtained by propagation from the initial bands. At each step multiple fire fronts can evolve simultaneously on the volumetric object. The flames of the fire are constructed from streams of particles whose movement is regulated by a velocity field generated with the hardware-accelerated Lattice Boltzmann Model (LBM). The LBM provides a physically-based simulation of the air flow around the burning object. The object voxels and the splats associated with the flame particles are rendered in the same pipeline so that the volume data with its external and internal structures can be displayed along with the fire.

Many clustering and layout techniques have been used for structuring and visualising complex data. This paper is inspired by a number of such contemporary techniques and presents a novel hybrid approach based upon stochastic sampling, interpolation and spring models. We use Chalmers' 1996 O(N2) spring model as a benchmark when evaluating our technique, comparing layout quality and run times using data sets of synthetic and real data. Our algorithm runs in O(N√N) and executes significantly faster than Chalmers' 1996 algorithm, whilst producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data.

We describe a new method for the visualization of tree structured relational data. It can be used especially for the display of very large hierarchies in a 2-dimensional space. We discuss the advantages and limitations of current techniques of tree visualization. Our strategy is to optimize the drawing of trees in a geometrical plane and maximize the utilization of display space by allowing more nodes and links to be displayed at a limit screen resolution. We use the concept of enclosure to partition the entire display space into a collection of local regions that are assigned to all nodes in tree T for the display of their sub-trees and themselves. To enable the exploration of large hierarchies, we use a modified semantic zooming technique to view the detail of a particular part of the hierarchy at a time based on user's interest. Layout animation is also provided to preserve the mental map while the user is exploring the hierarchy by changing zoomed views.

We present an extremely fast graph drawing algorithm for very large graphs, which we term ACE (for Algebraic multigrid Computation of Eigenvectors). ACE exhibits an improvement of something like two orders of magnitude over the fastest algorithms we are aware of; it draws graphs of millions of nodes in less than a minute. ACE finds an optimal drawing by minimizing a quadratic energy function. The minimization problem is expressed as a generalized eigenvalue problem, which is rapidly solved using a novel algebraic multigrid technique. The same generalized eigenvalue problem seems to come up also in other fields, hence ACE appears to be applicable outside of graph drawing too.

In this paper we present angular brushing for parallel coordinates (PC) as a new approach to highlighting rational data-properties, i.e., features which - in a non-separable way - depend on two data dimensions. We also demonstrate smooth brushing as an intuitive tool for specifying nonbinary degree-of-interest functions (for focus+context visualization). We also briefly describe our implementation as well as its application to the visualization of CFD data.

This paper introduces a new visualization method, the arc diagram, which is capable of representing complex patterns of repetition in string data. Arc diagrams improve over previous methods such as dotplots because they scale efficiently for strings that contain many instances of the same subsequence. This paper describes design and implementation issues related to arc diagrams and shows how they may be applied to visualize such diverse data as music, text, and compiled code.