2569639
Visibility culling for time-varying volume rendering using temporal occlusion coherence
Typically there is a high coherence in data values between neighboring time steps in an iterative scientific software simulation; this characteristic similarly contributes to a corresponding coherence in the visibility of volume blocks when these consecutive time steps are rendered. Yet traditional visibility culling algorithms were mainly designed for static data, without consideration of such potential temporal coherency. We explore the use of temporal occlusion coherence (TOC) to accelerate visibility culling for time-varying volume rendering. In our algorithm, the opacity of volume blocks is encoded by means of plenoptic opacity functions (POFs). A coherence-based block fusion technique is employed to coalesce time-coherent data blocks over a span of time steps into a single, representative block. Then POFs need only be computed for these representative blocks. To quickly determine the subvolumes that do not require updates in their visibility status for each subsequent time step, a hierarchical "TOC tree" data structure is constructed to store the spans of coherent time steps. To achieve maximal culling potential, while remaining conservative, we have extended our previous POP into an optimized POP (OPOP) encoding scheme for this specific scenario. To test our general TOC and OPOF approach, we have designed a parallel time-varying volume rendering algorithm accelerated by visibility culling. Results from experimental runs on a 32-processor cluster confirm both the effectiveness and scalability of our approach.
8122062
High quality splatting and volume synthesis
Our recent improvements to volume rendering using splatting allow for an accurate functional reconstruction and volume integration, occlusion-based acceleration, post-rendering classification and shading, 3D texture mapping, bump mapping, anti-aliasing and gaseous animation. A pure software implementation requiring low storage and using simple operations has been implemented to easily support these operations. This paper presents a unified framework and discussion of these enhancements, as well as extensions to support hypertextures efficiently. Discussions on appropriate volume models for effective usage of these techniques is also presented.
1676234
A complete distance field representation
Distance fields are an important volume representation. A high quality distance field facilitates accurate surface characterization and gradient estimation. However, due to Nyquist's law, no existing volumetric methods based on the linear sampling theory can fully capture surface details, such as comers and edges, in 3D space. We propose a novel complete distance field representation (CDFR) that does not rely on Nyquist's sampling theory. To accomplish this, we construct a volume where each voxel has a complete description of all portions of surface that affect the local distance field. For any desired distance, we are able to extract a surface contour in true Euclidean distance, at any level of accuracy, from the same CDFR representation. Such point-based iso-distance contours have faithful per-point gradients and can be interactively visualized using splatting, providing per-point shaded image quality. We also demonstrate applying CDFR to a cutting edge design for manufacturing application involving high-complexity parts at unprecedented accuracy using only commonly available computational resources.
2129354
Visualization of neuronal fiber connections from DT-MRI with global optimization
Diffusion Tensor MRI (DT-MRI) provides valuable 3D data describing diffusion characteristics of water molecules in the human brain. From DT-MRI, it is hoped that neuronal fiber connections among cortical regions can be reliably extracted and adequately interpreted. To achieve this goal, several significant challenges persist. In this paper, by means of dynamic programming we have developed a global fiber reconstruction algorithm enabling efficient visualization of neuronal connections queried by both the start and end points on the fly. Besides an inherent ability to handle noisy datasets, our algorithm also naturally addresses situations where neuronal fibers branch or cross each other. We demonstrate the efficacy of our approach with visualization of neuronal connections among activated brain cortical regions detected by functional MRI (fMRI).
36043890
Remote Visualization by Browsing Image Based Databases with Logistical Networking
2068223
Edge Preservation in Volume Rendering Using Splatting
This paper presents a method to preserve sharp edge details in splatting for volume rendering. Conventional splatting algorithms produce fuzzy images for views close to the volume model. The lack of details in such views greatly hinders study and manipulation of data sets using virtual navigation. Our method applies a non-linear warping to the footprints of conventional splat and builds a table of footprints for different possible edge positions and edge strengths. When rendering, we pick a footprint from the table for each splat, based on the relative position of the voxel to the closest edge. Encouraging results have been achieved both for synthetic data and medical data.
3062410
Conceptualizing the three dimensions of inter-organizational communities of practice
The paper describes and analyzes the way in which a particular case company stimulated the development of communities of practice as a vehicle for encouraging product and process innovation. Some unique features of these communities were explored. In particular the involvement of retired engineers and the inclusion of external parties were both found to contribute to the effectiveness of these communities. More specifically, the effectiveness of these communities were discussed in relation to three dimensions – structural, strategic and technological. Analysis of these three dimensions was useful in examining the role played by these communities within the case company. The paper concludes by considering the lessons for managers that can be drawn from this analysis. Newell,Huang,Galliers Conceptualizing the three dimensions of Inter-Organizational
2763612
Information Marginalization on Subgraphs
Real-world data often involves objects that exhibit multiple relationships; for example, ‘papers' and ‘authors' exhibit both paper-author interactions and paper-paper citation relationships. A typical learning problem requires one to make inferences about a subclass of objects (e.g. ‘papers'), while using the remaining objects and relations to provide relevant information. We present a simple, unified mechanism for incorporating information from multiple object types and relations when learning on a targeted subset. In this scheme, all sources of relevant information are marginalized onto the target subclass via random walks. We show that marginalized random walks can be used as a general technique for combining multiple sources of information in relational data. With this approach, we formulate new algorithms for transduction and ranking in relational data, and quantify the performance of new schemes on real world data—achieving good results in many problems.
9961789
Interactively visualizing procedurally encoded scalar fields
While interactive visualization of rectilinear gridded volume data sets can now be accomplished using texture mapping hardware on commodity PCs, interactive rendering and exploration of large scattered or unstructured data sets is still a challenging problem. We have developed a new approach that allows the interactive rendering and navigation of procedurally-encoded 3D scalar fields by reconstructing these fields on PC class graphics processing units. Since the radial basis functions (RBFs) we use for encoding can provide a compact representation of volumetric scalar fields, the large grid/mesh traditionally needed for rendering is no longer required and ceases to be a data transfer and computational bottleneck during rendering. Our new approach will interactively render RBF encoded data obtained from arbitrary volume data sets, including both structured volume models and unstructured scattered volume models. This procedural reconstruction of large data sets is flexible, extensible, and can take advantage of the Moore's Law cubed increase in performance of graphics hardware.
8956273
Hierarchical Diagnosis of Multiple Faults
Due to large search spaces, diagnosis of combinational circuits is often practical for finding only single and double faults. In principle, system models can be compiled into a tractable representation (such as DNNF) on which faults of arbitrary cardinality can be found efficiently. For large circuits, however, compilation can become a bottleneck due to the large number of variables necessary to model the health of individual gates. We propose a novel method that greatly reduces this number, allowing the compilation, as well as the diagnosis, to scale to larger circuits. The basic idea is to identify regions of a circuit, called cones, that are dominated by single gates, and model the health of each cone with a single health variable. When a cone is found to be possibly faulty, we diagnose it by again identifying the cones inside it, and so on, until we reach a base case. We show that results combined from these hierarchical sessions are sound and complete with respect to minimum-cardinality diagnoses. We implement this method on top of the diagnoser developed by Huang and Darwiche in 2005, and present evidence that it significantly improves the efficiency and scalability of diagnosis on the ISCAS-85 circuits.
7486661
The Language of Search
This paper is concerned with a class of algorithms that perform exhaustive search on propositional knowledge bases. We show that each of these algorithms defines and generates a propositional language. Specifically, we show that the trace of a search can be interpreted as a combinational circuit, and a search algorithm then defines a propositional language consisting of circuits that are generated across all possible executions of the algorithm. In particular, we show that several versions of exhaustive DPLL search correspond to such well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF. By thus mapping search algorithms to propositional languages, we provide a uniform and practical framework in which successful search techniques can be harnessed for compilation of knowledge into various languages of interest, and a new methodology whereby the power and limitations of search algorithms can be understood by looking up the tractability and succinctness of the corresponding propositional languages.