1794602
HDR VolVis: high dynamic range volume visualization
In this paper, we present an interactive high dynamic range volume visualization framework (HDR VolVis) for visualizing volumetric data with both high spatial and intensity resolutions. Volumes with high dynamic range values require high precision computing during the rendering process to preserve data precision. Furthermore, it is desirable to render high resolution volumes with low opacity values to reveal detailed internal structures, which also requires high precision compositing. High precision rendering will result in a high precision intermediate image (also known as high dynamic range image). Simply rounding up pixel values to regular display scales will result in loss of computed details. Our method performs high precision compositing followed by dynamic tone mapping to preserve details on regular display devices. Rendering high precision volume data requires corresponding resolution in the transfer function. To assist the users in designing a high resolution transfer function on a limited resolution display device, we propose a novel transfer function specification interface with nonlinear magnification of the density range and logarithmic scaling of the color/opacity range. By leveraging modern commodity graphics hardware, multiresolution rendering techniques and out-of-core acceleration, our system can effectively produce an interactive visualization of large volume data, such as 2.048/sup 3/.
13005280
High dynamic range volume visualization
High resolution volumes require high precision compositing to preserve detailed structures. This is even more desirable for volumes with high dynamic range values. After the high precision intermediate image has been computed, simply rounding up pixel values to regular display scales loses the computed details. In this paper, we present a novel high dynamic range volume visualization method for rendering volume data with both high spatial and intensity resolutions. Our method performs high precision volume rendering followed by dynamic tone mapping to preserve details on regular display devices. By leveraging available high dynamic range image display algorithms, this dynamic tone mapping can be automatically adjusted to enhance selected features for the final display. We also present a novel transfer function design interface with nonlinear magnification of the density range and logarithmic scaling of the color/opacity range to facilitate high dynamic range volume visualization. By leveraging modern commodity graphics hardware and out-of-core acceleration, our system can produce an effective visualization of huge volume data.
16773627
Illustrating surfaces in volume
This paper presents a novel framework for illustrating surfaces in a volume. Surfaces are illustrated by drawing only feature lines, such as silhouettes, valleys, ridges, and surface hatching strokes, and are embedded in volume renderings. This framework promises effective illustration of both surfaces and volumes without occluding or cluttering each other. A two-step approach has been taken: the first step depicts surfaces; the second step performs volume rendering, at the same time embedding surfaces from the first step.
 We introduce Procedurally Perturbed Image Processing (PIP), a new method for enhancing both feature detection and depiction of surfaces. We also present implementation strategies, especially those leveraging modern graphics hardware, for delivering an interactive rendering system. Our implementation results have shown that this mixed form of rendering improves volume visualization and is efficient.
9140729
INSPIRE: an interactive image assisted non-photorealistic rendering system
We present a GPU supported interactive non-photorealistic rendering system, INSPIRE, which performs feature extraction in both image space, on intermediately rendered images, and object space, on models of various representations, e.g., point, polygon, or hybrid models, without needing connectivity information. INSPIRE obtains interactive NPR rendering with most styles of existing NPR systems, but offers more flexibility on model representations and compromises little on rendering speed.
6386106
Procedural Image Processing for Visualization
We present a novel Procedural Image Processing (PIP) method and demonstrate its applications in visualization. PIP modulates the sampling positions of a conventional image processing kernel (e.g. edge detection filter) through a procedural perturbation function. When properly designed, PIP can produce a variety of styles for edge depiction, varying on width, solidity, and pattern, etc. In addition to producing artistic stylization, in this paper we demonstrate that PIP can be employed to achieve various visualization tasks, such as contour enhancement, focus+context visualization, importance driven visualization and uncertainty visualization. 
 
PIP produces unique effects that often either cannot be easily achieved through conventional filters or would require multiple pass filtering. PIP perturbation functions are either defined by analytical expressions or encoded in pre-generated images. We leverage the programmable fragment shader of the current graphics hardware for achieving the operations in real-time.
18808395
Stippling and silhouettes rendering in geometry-image space
We present a novel non-photorealistic rendering method that performs all operations in a geometry-image domain. We first apply global conformal parameterization to the input geometry model and generate corresponding geometry images. Strokes and silhouettes are then computed in the geometry-image domain. The geometry-image space provides combined benefits of the existing image space and object space approaches. It allows us to take advantage of the regularity of 2D images and yet still have full access to the object geometry information. A wide range of image processing tools can be leveraged to assist various operations involved in achieving non-photorealistic rendering with coherence.
88596
Using GUI Run-Time State as Feedback to Generate Test Cases
This paper presents a new automated model-driven technique to generate test cases by using feedback from the execution of a "seed test suite" on an application under test (AUT). The test cases in the seed suite are designed to be generated automatically and executed very quickly. During their execution, feedback obtained from the AUT's run-time state is used to generate new, "improved" test cases. The new test cases subsequently become part of the seed suite. This "anytime technique" continues iteratively, generating and executing additional test cases until resources are exhausted or testing goals have been met. The feedback-based technique is demonstrated for automated testing of graphical user interfaces (GUIs). An existing abstract model of the GUI is used to automatically generate the seed test suite. It is executed; during its execution, state changes in the GUI pinpoint important relationships between GUI events, which evolve the model and help to generate new test cases. Together with a reverse- engineering algorithm used to obtain the initial model and seed suite, the feedback-based technique yields a fully automatic, end-to-end GUI testing process. A feasibility study on four large fielded open-source software (OSS) applications demonstrates that this process is able to significantly improve existing techniques and help identify/report serious problems in the OSS. In response, these problems have been fixed by the developers of the OSS in subsequent versions.
124461239
Subband noise estimation for adaptive wavelet shrinkage
In this article, we present an adaptive image denoising method based on subband noise modeling. For wavelet shrinkage, choosing the threshold depends on correctly estimating the noise variance. By modeling the inter-subband noise variance with a parameterized normalized exponential function, the problem becomes identifying the maximum noise variance. Such a maximum exists in the highest decomposition level and can be estimated by locating the extreme of the first derivative of the subband variance function. The experiments demonstrate that our method outperforms peers, especially in the cases of large noise variance.
6360330
An empirical study of reliable multicast protocols over Ethernet-connected networks
Recent advances in multicasting over the Internet present new opportunities for improving communication performance in clusters of workstations. The standard IP multicast, however only supports unreliable multicast, which is difficult to use for building high level message passing routines. Thus, reliable multicast primitives must be implemented over the standard IP multicast to facilitate the use of multicast for high performance communication on clusters of workstations. Although many reliable multicast protocols have been proposed for the wide area Internet environment, the impact of architectural features of local area networks (LANs) on the reliable multicast protocols has not been thoroughly studied. Efficient reliable multicast protocols for LANs must exploit these features to achieve the best performance. In this paper we study four types of reliable multicast protocols: the ACK-based protocols, the NAK-based protocols with polling, the ring-based protocols, and the tree-based protocols. We evaluate the performance of the protocols over Ethernet-connected networks, study the impact of architectural features of the Ethernet on the performance of the protocols, and investigate the methods to exploit these features to achieve the best performance.
2765235
Volumetric tongue reconstruction by fusing bidirectional MR images
In this paper we describe a 3D reconstruction method that fuses sagittal and coronal images. In our volumetric MRI study of the human tongue, multiple contiguous slices were acquired in the sagittal and the coronal planes. A bidirectional linear interpolation method was developed that estimated target voxel value using weighted neighbor pixels in the bounding slices. A wavelet fusion method was then employed to integrate prominent subband coefficients. Experiments have shown that fusion-based image reconstruction reduced artifacts, greatly improved reconstruction accuracy, and enhanced image details. Our method casts new light on MR imaging and image processing that demand high resolution in a short acquisition time
8920686
Population Sizing Based on Landscape Feature
Population size for EvolutionaryAlgorithms is usually an empirical parameter. We study the population size from aspects of fitness landscapes' ruggedness and Probably Approximately Correct (PAC) learning theory.