The analysis and visualization of data created from simulations on modern supercomputers is a daunting challenge because the incredible compute power of modern supercomputers allow scientists to generate datasets with very high spatial and temporal resolutions. The limited bandwidth and capacity of networking and storage devices connecting supercomputers to analysis machines become the major bottleneck for data analysis such that simply moving the whole dataset from the supercomputer to a data analysis machine is infeasible. A common approach to visualize high temporal resolution simulation datasets under constrained I/O is to reduce the sampling rate in the temporal domain while preserving the original spatial resolution at the time steps. Data interpolation between the sampled time steps alone may not be a viable option since it may suffer from large errors, especially when using a lower sampling rate. We present a novel ray-based representation storing ray based histograms and depth information that recovers the evolution of volume data between sampled time steps. Our view-dependent proxy allows for a good trade off between compactly representing the time-varying data and leveraging temporal coherence within the data by utilizing interpolation between time steps, ray histograms, depth information, and codebooks. Our approach is able to provide fast rendering in the context of transfer function exploration to support visualization of feature evolution in time-varying data.

Analyzing scientific datasets created from simulations on modern supercomputers is a daunting challenge due to the fast pace at which these datasets continue to grow. Low cost post analysis machines used by scientists to view and analyze these massive datasets are severely limited by their deficiencies in storage bandwidth, capacity, and computational power. Trying to simply move these datasets to these platforms is infeasible. Any approach to view and analyze these datasets on post analysis machines will have to effectively address the inevitable problem of data loss. Image based approaches are well suited for handling very large datasets on low cost platforms. Three challenges with these approaches are how to effectively represent the original data with minimal data loss, analyze the data in regards to transfer function exploration, which is a key analysis tool, and quantify the error from data loss during analysis. We present a novel image based approach using distributions to preserve data integrity. At each view sample, view dependent data is summarized at each pixel with distributions to define a compact proxy for the original dataset. We present this representation along with how to manipulate and render large scale datasets on post analysis machines. We show that our approach is a good trade off between rendering quality and interactive speed and provides uncertainty quantification for the information that is lost.

The size of large-scale scientific datasets created from simulations and computed on modern supercomputers continues to grow at a fast pace. A daunting challenge is to analyze and visualize these intractable datasets on commodity hardware. A recent and promising area of research is to replace the dataset with a distribution based proxy representation that summarizes scalar information into a much reduced memory footprint. Proposed representations subdivide the dataset into local blocks, where each block holds important statistical information, such as a histogram. A key drawback is that a distribution representing the scalar values in a block lacks spatial information. This manifests itself as large errors in visualization algorithms. We present a novel statistically-based representation by augmenting the block-wise distribution based representation with location information, called a value-based spatial distribution. Information from both spatial and scalar spaces are combined using Bayes' rule to accurately estimate the data value at a given spatial location. The representation is compact using the Gaussian Mixture Model. We show that our approach is able to preserve important features in the data and alleviate uncertainty.

Splatting is widely applied in many areas, including volume, point-based and image-based rendering. Improvements to splatting, such as eliminating popping and color bleeding, occasion-based acceleration, post-rendering classification and shading, have all been recently accomplished. These improvements share a common need for efficient frame-buffer accesses. We present an optimized software splatting package, using a newly designed primitive, called FastSplat, to scan-convert footprints. Our approach does not use texture mapping hardware, but supports the whole pipeline in memory. In such an integrated pipeline, we are then able to study the optimization strategies and address image quality issues. While this research is meant for a study of the inherent trade-off of splatting, our renderer, purely in software, achieves 3- to 5-fold speedups over a top-end texture hardware implementation (for opaque data sets). We further propose a method of efficient occlusion culling using a summed area table of opacity. 3D solid texturing and bump mapping capabilities are demonstrated to show the flexibility of such an integrated rendering pipeline. A detailed numerical error analysis, in addition to the performance and storage issues, is also presented. Our approach requires low storage and uses simple operations. Thus, it is easily implementable in hardware.

Advances in visualization technology and specialized graphic workstations allow clinicians to virtually interact with anatomical structures contained within sampled medical-image datasets. A hindrance to the effective use of this technology is the difficult problem of image segmentation. In this paper, we utilize a recently proposed oscillator network called the locally excitatory globally inhibitory oscillator network (LEGION) whose ability to achieve fast synchrony with local excitation and desynchrony with global inhibition makes it an effective computational framework for grouping similar features and segregating dissimilar ones in an image. We extract an algorithm from LEGION dynamics and propose an adaptive scheme for grouping. We show results of the algorithm to two-dimensional (2-D) and three-dimensional (3-D) (volume) computerized topography (CT) and magnetic resonance imaging (MRI) medical-image datasets. In addition, we compare our algorithm with other algorithms for medical-image segmentation, as well as with manual segmentation. LEGION's computational and architectural properties make it a promising approach for real-time medical-image segmentation.

Splatting is a popular volume rendering algorithm that pairs good image quality with an efficient volume projection scheme. The current axis-aligned sheet-buffer approach, however, bears certain inaccuracies. The effect of these is less noticeable in still images, but clearly revealed in animated viewing, where disturbing popping of object brightness occurs at certain view angle transitions. In previous work, we presented a new variant of sheet-buffered splatting in which the compositing sheets are oriented parallel to the image plane. This scheme not only eliminates the condition for popping, but also produces images of higher quality. In this paper, we summarize this new paradigm and extend it in a number of ways. We devise a new solution to render rectilinear grids of equivalent cost to the traditional approach that treats the anisotropic volume as being warped into a cubic grid. This enables us to use the usual radially symmetric kernels, which can be projected without inaccuracies. Next, current splatting approaches necessitate the projection of all voxels in the iso-interval(s), although only a subset of these voxels may eventually be visible in the final image. To eliminate these wasteful computations we propose a novel front-to-back approach that employs an occlusion map to determine if a splat contributes to the image before it is projected, thus skipping occluded splats. Additional measures are presented for further speedups. In addition, we present an efficient list-based volume traversal scheme that facilitates the quick modification of transfer functions and iso-values.

The paper describes three new results for volume rendering algorithms utilizing splatting. First, an antialiasing extension to the basic splatting algorithm is introduced that mitigates the spatial aliasing for high resolution volumes. Aliasing can be severe for high resolution volumes or volumes where a high depth of field leads to converging samples along the perspective axis. Next, an analysis of the common approximation errors in the splatting process for perspective viewing is presented. In this context, we give different implementations, distinguished by efficiency and accuracy, for adding the splat contributions to the image plane. We then present new results in controlling the splatting errors and also show their behavior in the framework of our new antialiasing technique. Finally, current work in progress on extensions to splatting for temporal antialiasing is demonstrated. We present a simple but highly effective scheme for adding motion blur to fast moving volumes.

Splatting is a popular direct volume rendering algorithm. However, the algorithm does not correctly render cases where the volume sampling rate is higher than the image sampling rate (e.g. more than one voxel maps into a pixel). This situation arises with orthographic projections of high-resolution volumes, as well as with perspective projections of volumes of any resolution. The result is potentially severe spatial and temporal aliasing artifacts. Some volume ray-casting algorithms avoid these artifacts by employing reconstruction kernels which vary in width as the rays diverge. Unlike ray-casting algorithms, existing splatting algorithms do not have an equivalent mechanism for avoiding these artifacts. The authors propose such a mechanism, which delivers high-quality splatted images and has the potential for a very efficient hardware implementation.