Illustrative context-preserving exploration of volume data

In volume rendering, it is very difficult to simultaneously visualize interior and exterior structures while preserving clear shape cues. Highly transparent transfer functions produce cluttered images with many overlapping structures, while clipping techniques completely remove possibly important context information. In this paper, we present a new model for volume rendering, inspired by techniques from illustration. It provides a means of interactively inspecting the interior of a volumetric data set in a feature-driven way which retains context information. The context-preserving volume rendering model uses a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. This new method represents an alternative to conventional clipping techniques, sharing their easy and intuitive user control, but does not suffer from the drawback of missing context information     

Volumetric texture synthesis for non-photorealistic volume rendering of medical data

This paper presents a novel technique, called volumetric texture synthesis, for non-photorealistic volume rendering. It extends texture synthesis from 2D areas/3D surfaces to volumes. By selecting different texture samples, it allows for a wide variety of stylized rendering for the target volume. As a preprocessing step, volume data analysis is used to identify texture orientations for the volume. This is followed by volumetric texture synthesis, which generates 3D non-photorealistic textures along the identified texture orientations. Finally, standard volume rendering is applied to display the volume data decorated by the texture. Experimental results are provided in the paper.     

Classification of imprecise data using interval Fisher discriminator

In this paper, an imprecise data classification is considered using new version of Fisher discriminator, namely interval Fisher. In the conventional formulation of Fisher, elements of within‐class scatter matrix (related to covariance matrix between clusters) and between‐class scatter matrix (related to covariance matrix of centers of clusters) have single values; but in the interval Fisher, the elements of matrices are in the interval form and can vary in a range. The particle swarm optimization search method is used for solving a constrained optimization problem of the interval Fisher discriminator. Unlike conventional Fisher with one optimal hyperplane, interval Fisher gives two optimal hyperplanes thereupon three decision regions are obtained. Two classes with regard to imprecise scatter matrices are derived by decision making using these optimal hyperplanes. Also, fuzzy region lets us help in fuzzy decision over input test samples. Unlike a support vector classifier with two parallel hyperplanes, interval Fisher generally gives us two nonparallel hyperplanes. Experimental results show the suitability of this idea. © 2011 Wiley Periodicals, Inc.     

3DIVE: An Immersive Environment for Interactive Volume Data Exploration

This paper describes an immersive system,called 3DIVE,for interactive volume data visualization and exploration inside the CAVE virtual environment.Combining interactive volume rendering and virtual reality provides a netural immersive environment for volumetric data visualization.More advanced data exploration operations,such as object level data manipulation,simulation and analysis ,are supported in 3DIVE by several new techniques,In particular,volume primitives and texture regions ae used for the rendering,manipulation,and collision detection of volumetric objects;and the region-based rendering pipeline is integrated with 3D image filters to provide an image-based mechanism for interactive transfer function design.The system has been recently released as public domain software for CAVE/ImmersaDesk users,and is currently being actively used by various scientific and biomedical visualization projects.     

Direct interval volume visualization

We extend direct volume rendering with a unified model for generalized isosurfaces, also called interval volumes, allowing a wider spectrum of visual classification. We generalize the concept of scale-invariant opacity—typical for isosurface rendering—to semi-transparent interval volumes. Scale-invariant rendering is independent of physical space dimensions and therefore directly facilitates the analysis of data characteristics. Our model represents sharp isosurfaces as limits of interval volumes and combines them with features of direct volume rendering. Our objective is accurate rendering, guaranteeing that all isosurfaces and interval volumes are visualized in a crack-free way with correct spatial ordering. We achieve simultaneous direct and interval volume rendering by extending preintegration and explicit peak finding with data-driven splitting of ray integration and hybrid computation in physical and data domains. Our algorithm is suitable for efficient parallel processing for interactive applications as demonstrated by our CUDA implementation.     

Interactive exploration of tensor fields in geosciences using volume rendering

Volume rendering methods enable the user to explore interactively scalar data on regularly spaced three-dimensional grids. This article discusses how to use this method to explore and analyse three-dimensional tensor fields. The proposed visualization makes use of the programmability of modern graphics hardware and of “Line Integral Convolution”, a texture-based technique for the visualization of vector fields. While an example from geomechanics is used for presentation purposes, the rendering methods introduced are generic and would suit other application areas that involve volumetric data with several attributes equally well.     

Interactive volume exploration for feature detection and quantification in industrial CT data

This paper presents a novel method for interactive exploration of industrial CT volumes such as cast metal parts, with the goal of interactively detecting, classifying, and quantifying features using a visualization-driven approach. The standard approach for defect detection builds on region growing, which requires manually tuning parameters such as target ranges for density and size, variance, as well as the specification of seed points. If the results are not satisfactory, region growing must be performed again with different parameters. In contrast, our method allows interactive exploration of the parameter space, completely separated from region growing in an unattended pre-processing stage. The pre-computed feature volume tracks a feature size curve for each voxel over time, which is identified with the main region growing parameter such as variance. A novel 3D transfer function domain over (density, feature size, time) allows for interactive exploration of feature classes. Features and feature size curves can also be explored individually, which helps with transfer function specification and allows coloring individual features and disabling features resulting from CT artifacts. Based on the classification obtained through exploration, the classified features can be quantified immediately.     

Relation-aware volume exploration pipeline

Volume exploration is an important issue in scientific visualization. Research on volume exploration has been focused on revealing hidden structures in volumetric data. While the information of individual structures or features is useful in practice, spatial relations between structures are also important in many applications and can provide further insights into the data. In this paper, we systematically study the extraction, representation, exploration, and visualization of spatial relations in volumetric data and propose a novel relation-aware visualization pipeline for volume exploration. In our pipeline, various relations in the volume are first defined and measured using region connection calculus (RCC) and then represented using a graph interface called relation graph. With RCC and the relation graph, relation query and interactive exploration can be conducted in a comprehensive and intuitive way. The visualization process is further assisted with relation-revealing viewpoint selection and color and opacity enhancement. We also introduce a quality assessment scheme which evaluates the perception of spatial relations in the rendered images. Experiments on various datasets demonstrate the practical use of our system in exploratory visualization.     

Fast ray-tracing of rectilinear volume data using distancetransforms

The paper discusses and experimentally compares distance based acceleration algorithms for ray tracing of volumetric data with an emphasis on the Chessboard Distance (CD) voxel traversal. The acceleration of this class of algorithms is achieved by skipping empty macro regions, which are defined for each background voxel of the volume. Background voxels are labeled in a preprocessing phase by a value, defining the macro region size, which is equal to the voxel distance to the nearest foreground voxel. The CD algorithm exploits the chessboard distance and defines the ray as a nonuniform sequence of samples positioned at voxel faces. This feature assures that no foreground voxels are missed during the scene traversal. Further, due to parallelepipedal shape of the macro region, it supports accelerated visualization of cubic, regular, and rectilinear grids. The CD algorithm is suitable for all modifications of the ray tracing/ray casting techniques being used in volume visualization and volume graphics. However, when used for rendering based on local surface interpolation, it also enables fast search of intersections between rays and the interpolated surface, further improving speed of the process     

Solid modelling using a volume encoding data structure

A strong emphasis has lately been put in research to develop methods for Solid Modelling using surface or volume encoding data structures instead of conventional edge-face-vertex or polyhedral representations for 3-D objects.This article describes a Solid Modelling system using a volume encoding data structure called OCTREE which divides a solid into cubes.     

SQL extension for interval data

IXSQL, an extension to SQL, is proposed for the management of interval data. IXSQL is syntactically and semantically upwards consistent with SQL2. Its specification has been based both on theoretical results and actual user requirements for the management of temporal data, a special case of interval data. Design decisions and implementation issues are also discussed     

Smooth surface extraction from unstructured point-based volume data using PDEs

Smooth surface extraction using partial differential equations (PDEs) is a well-known and widely used technique for visualizing volume data. Existing approaches operate on gridded data and mainly on regular structured grids. When considering unstructured point-based volume data where sample points do not form regular patterns nor are they connected in any form, one would typically resample the data over a grid prior to applying the known PDE-based methods. We propose an approach that directly extracts smooth surfaces from unstructured point-based volume data without prior resampling or mesh generation. When operating on unstructured data one needs to quickly derive neighborhood information. The respective information is retrieved by partitioning the 3D domain into cells using a kd-tree and operating on its cells. We exploit neighborhood information to estimate gradients and mean curvature at every sample point using a four-dimensional least-squares fitting approach. Gradients and mean curvature are required for applying the chosen PDE-based method that combines hyperbolic advection to an isovalue of a given scalar field and mean curvature flow. Since we are using an explicit time-integration scheme, time steps and neighbor locations are bounded to ensure convergence of the process. To avoid small global time steps, we use asynchronous local integration. We extract the surface by successively fitting a smooth auxiliary function to the data set. This auxiliary function is initialized as a signed distance function. For each sample and for every time step we compute the respective gradient, the mean curvature, and a stable time step. With these informations the auxiliary function is manipulated using an explicit Euler time integration. The process successively continues with the next sample point in time. If the norm of the auxiliary function gradient in a sample exceeds a given threshold at some time, the auxiliary function is reinitialized to a signed distance function. After convergence of the evolution, the resulting smooth surface is obtained by extracting the zero isosurface from the auxiliary function using direct isosurface extraction from unstructured point-based volume data and rendering the extracted surface using point-based rendering methods.     

Matrix games with interval data

The conventional game theory is concerned with how rational individuals make decisions when they are faced with known payoffs. In the real world, sometimes the payoffs are not known and have to be estimated, and sometimes the payoffs are only approximately known. This paper develops a solution method for the two-person zero-sum game where the payoffs are imprecise and are represented by interval data. Since the payoffs are imprecise, the value of the game should be imprecise as well. A pair of two-level mathematical programs is formulated to obtain the upper bound and lower bound of the value of the game. Based on the duality theorem and by applying a variable substitution technique, the pair of two-level mathematical programs is transformed to a pair of ordinary one-level linear programs. Solving the pair of linear programs produces the interval of the value of the game. It is shown that the two players in the game have the same upper bound and lower bound for the value of the imprecise game. An example illustrates the whole idea and sheds some light on imprecise game.     

Management of interval probabilistic data

In this paper we present a data model for uncertain data, where uncertainty is represented using interval probabilities. The theory introduced in the paper can be applied to different specific data models, because the entire approach has been developed independently of the kind of manipulated objects, like XML documents, relational tuples, or other data types. As a consequence, our theory can be used to extend existing data models with the management of uncertainty. In particular, the data model we obtain as an application to XML data is the first proposal that combines XML, interval probabilities and a powerful query algebra with selection, projection, and cross product. The cross product operator is not based on assumptions of independence between XML trees from different collections. Being defined with a possible worlds semantics, our operators are proper extensions of their traditional counterparts, and reduce to them when there is no uncertainty. The main practical result of the paper is a set of equivalences that can be used to compare or rewrite algebraic queries on interval probabilistic data, in particular XML and relational.