“We’re searching here, trying to get away from the cut and dried handling of things all the way through—everything—and the only way to do it is to leave things open until we have completely explored every bit of it.” –Walt Disney Researchers in nonphotorealistic rendering (NPR) have investigated a variety of techniques to simulate the styles of artists. Recent work has resulted in methods for pen-and-ink illustration, pencil sketching, watercolor, engraving, and silhouette edge rendering. This paper presents real-time methods to emulate cartoon styles. We also present variations on a texture mapping technique to achieve real-time pencil sketching. We demonstrate our method of inking silhouettes, material and mesh boundaries, and crease edges. In addition, we present techniques for emphasizing motion of cartoon objects by introducing geometry into the cartoon scene. The rendering system is integrated with an animation system and a runtime multiresolution mesh (MRM) system to achieve scalability, ensuring real-time performance on any platform. Such solutions allow us to take advantage of evolving hardware in order to make nonphotorealistic animation and rendering achievable on low- and high-end consumer platforms. All of the techniques described can be applied to models created with standard modeling tools and require no additional mark-up information from the modeler.

We present a new rendering technique for processing multiple multiresolution textures of LOD terrain models and describe its application to interactive, animated terrain content design. The approach is based on a multiresolution model for terrain texture which cooperates with a multiresolution model for terrain geometry. For each texture layer, an image pyramid and a texture tree are constructed. Multiple texture layers can be associated with one terrain model and can be combined in different ways, e.g., by blending and masking. The rendering algorithm traverses simultaneously the geometry multiresolution model and the texture multiresolution model, and takes into account geometric and texture approximation errors. It uses multi-pass rendering and exploits multitexturing to achieve real-time performance. Applications include interactive texture lenses, texture animation, and topographic textures. These techniques offer an enormous potential for developing new visualization applications...

One frequently cited reason for the lack of wide deployment of cryptographic protocols is the (perceived) poor performance of the algorithms they employ and their impact on the rest of the system. Although high-performance dedicated cryptographic accelerator cards have been commercially available for some time, market penetration remains low. We take a different approach, seeking to exploit existing system resources, such as Graphics Processing Units (GPUs) to accelerate cryptographic processing.

A major limitation of the use of endoscopes in minimally invasive surgery is the lack of relative context between the endoscope and its surroundings. The purpose of this work was to fuse images obtained from a tracked endoscope to surfaces derived from 3D pre-operative MR or CT data, for assistance in surgical planning, training and guidance. To test our methods, we extracted polygonal surfaces from pre-operative CT images of a standard brain phantom (Kilgore Inc), and digitized endoscopic video images from a tracked neuro-endoscope. The optical properties of the endoscope were characterized using a simple calibration procedure. Registration of the phantom (physical space) and CT images (preoperative image space) was accomplished using fiducial markers that could be identified both on the phantom and within the images. The endoscopic images were corrected for radial lens distortion, and then mapped onto the extracted surfaces via a novel texture-mapping algorithm. The optical tracker has an accuracy of about 0.3 mm at its centroid, which allows the endoscope tip to be localized to within 1.0 mm. The mapping operation allows the endoscopic images to be painted onto the surfaces as they are acquired. Our method allows panoramic and stereoscopic visualization and navigation of the painted surface from arbitrary perspectives.

Abstract. Software that covertly monitors user actions, also known as spyware, has become a first-level security threat due to its ubiquity and the difficulty of detecting and removing it. Such software may be inadvertently installed by a user that is casually browsing the web, or may be purposely installed by an attacker or even the owner of a system. This is particularly problematic in the case of utility computing, early manifestations of which are Internet cafes and thin-client computing. Traditional trusted computing approaches offer a partial solution to this by significantly increasing the size of the trusted computing base (TCB) to include the operating system and other software. We examine the problem of protecting a user accessing specific services in such an environment. We focus on secure video broadcasts and remote desktop access when using any convenient, and often untrusted, terminal as two example applications. We posit that, at least for such applications, the TCB can be confined to a suitably modified graphics processing unit (GPU). Specifically, to prevent spyware on untrusted clients from accessing the user’s data, we restrict the boundary of trust to the client’s GPU by moving image decryption into GPUs. We use the GPU in order to leverage existing capabilities as opposed to designing a new component from scratch. We discuss the applicability of GPU-based decryption in these two sample scenarios and identify the limitations of the current generation of GPUs. We propose straightforward modifications to future GPUs that will allow the realization of the full approach. 1

City models with building exteriors, some interior spaces, vegetation, addresses and phototexture are of growing interest. Our work is the result of an Austrian national research program within the framework of Virtual Reality and Visualization. We describe a data collection system for buildings, a data modeling and visualization component.

We present a set of visualization methods for the analysis of multivariate data recorded from the measurement of the performance of athletes during training. We use a modified training device to measure the force, acceleration, displacement, and speed of the athlete’s feet and arms while performing a certain training exercise. We are interested in visually measuring and comparing the performance over several training sessions of the same and/or different athletes. For this, we adapt and extend several visualization methods for multivariate data. First, we use an enhanced signal plot and statistics plot to visualize the regularity of repetitions within a given exercise. Second, we use a novel texture-based signal plot to eliminate signal noise and emphasize the average repetitive pattern of the exercise. Finally, we use a signal clustering technique, visualized with a matrix plot, to detect similar exercises over long periods of time. We demonstrate our approaches with actual data from training sessions of several athletes. KEY WORDS Multivariate visualization, information visualization, visual analytics 1