DevLan/vispubs huggingface dataset:
Anisotropic Ambient Volume Shading
Authors: Marco Ament, Carsten Dachsbacher
Published at SciVis, 2015

We present a novel method to compute anisotropic shading for direct volume rendering to improve the perception of the orientation and shape of surface-like structures. We determine the scale-aware anisotropy of a shading point by analyzing its ambient region. We sample adjacent points with similar scalar values to perform a principal component analysis by computing the eigenvectors and eigenvalues of the covariance matrix. In particular, we estimate the tangent directions, which serve as the tangent frame for anisotropic bidirectional reflectance distribution functions. Moreover, we exploit the ratio of the eigenvalues to measure the magnitude of the anisotropy at each shading point. Altogether, this allows us to model a data-driven, smooth transition from isotropic to strongly anisotropic volume shading. In this way, the shape of volumetric features can be enhanced significantly by aligning specular highlights along the principal direction of anisotropy. Our algorithm is independent of the transfer function, which allows us to compute all shading parameters once and store them with the data set. We integrated our method in a GPU-based volume renderer, which offers interactive control of the transfer function, light source positions, and viewpoint. Our results demonstrate the benefit of anisotropic shading for visualization to achieve data-driven local illumination for improved perception compared to isotropic shading.
Association Analysis for Visual Exploration of Multivariate Scientific Data Sets
Authors: Xiaotong Liu, Han-Wei Shen
Published at SciVis, 2015

The heterogeneity and complexity of multivariate characteristics poses a unique challenge to visual exploration of multivariate scientific data sets, as it requires investigating the usually hidden associations between different variables and specific scalar values to understand the data's multi-faceted properties. In this paper, we present a novel association analysis method that guides visual exploration of scalar-level associations in the multivariate context. We model the directional interactions between scalars of different variables as information flows based on association rules. We introduce the concepts of informativeness and uniqueness to describe how information flows between scalars of different variables and how they are associated with each other in the multivariate domain. Based on scalar-level associations represented by a probabilistic association graph, we propose the Multi-Scalar Informativeness-Uniqueness (MSIU) algorithm to evaluate the informativeness and uniqueness of scalars. We present an exploration framework with multiple interactive views to explore the scalars of interest with confident associations in the multivariate spatial domain, and provide guidelines for visual exploration using our framework. We demonstrate the effectiveness and usefulness of our approach through case studies using three representative multivariate scientific data sets.
Auto-Calibration of Multi-Projector Displays with a Single Handheld Camera
Authors: Sanghun Park, Hyunggoog Seo, Seunghoon Cha, Jun-yong Noh
Published at SciVis, 2015

We present a novel approach that utilizes a simple handheld camera to automatically calibrate multi-projector displays. Most existing studies adopt active structured light patterns to verify the relationship between the camera and the projectors. The utilized camera is typically expensive and requires an elaborate installation process depending on the scalability of its applications. Moreover, the observation of the entire area by the camera is almost impossible for a small space surrounded by walls as there is not enough distance for the camera to capture the entire scene. We tackle these issues by requiring only a portion of the walls to be visible to a handheld camera that is widely used these days. This becomes possible by the introduction of our new structured light pattern scheme based on a perfect submap and a geometric calibration that successfully utilizes the geometric information of multi-planar environments. We demonstrate that immersive display in a small space such as an ordinary room can be effectively created using images captured by a handheld camera.
Automated visualization workflow for simulation experiments
Authors: Jonathan Leidig, Santhosh Dharmapuri
Published at SciVis, 2015

Modeling and simulation is often used to predict future events and plan accordingly. Experiments in this domain often produce thousands of results from individual simulations, based on slightly varying input parameters. Geo-spatial visualizations can be a powerful tool to help health researchers and decision-makers to take measures during catastrophic and epidemic events such as Ebola outbreaks. The work produced a web-based geo-visualization tool to visualize and compare the spread of Ebola in the West African countries Ivory Coast and Senegal based on multiple simulation results. The visualization is not Ebola specific and may visualize any time-varying frequencies for given geo-locations.
CAST: Effective and Efficient User Interaction for Context-Aware Selection in 3D Particle Clouds
Authors: Lingyun Yu 0001, Konstantinos Efstathiou 0001, Petra Isenberg, Tobias Isenberg 0001
Published at SciVis, 2015

We present a family of three interactive Context-Aware Selection Techniques (CAST) for the analysis of large 3D particle datasets. For these datasets, spatial selection is an essential prerequisite to many other analysis tasks. Traditionally, such interactive target selection has been particularly challenging when the data subsets of interest were implicitly defined in the form of complicated structures of thousands of particles. Our new techniques SpaceCast, TraceCast, and PointCast improve usability and speed of spatial selection in point clouds through novel context-aware algorithms. They are able to infer a user's subtle selection intention from gestural input, can deal with complex situations such as partially occluded point clusters or multiple cluster layers, and can all be fine-tuned after the selection interaction has been completed. Together, they provide an effective and efficient tool set for the fast exploratory analysis of large datasets. In addition to presenting Cast, we report on a formal user study that compares our new techniques not only to each other but also to existing state-of-the-art selection methods. Our results show that Cast family members are virtually always faster than existing methods without tradeoffs in accuracy. In addition, qualitative feedback shows that PointCast and TraceCast were strongly favored by our participants for intuitiveness and efficiency.
Cluster Analysis of Vortical Flow in Simulations of Cerebral Aneurysm Hemodynamics
Authors: Steffen Oeltze-Jafra, Juan R. Cebral, Gábor Janiga, Bernhard Preim
Published at SciVis, 2015

Computational fluid dynamic (CFD) simulations of blood flow provide new insights into the hemodynamics of vascular pathologies such as cerebral aneurysms. Understanding the relations between hemodynamics and aneurysm initiation, progression, and risk of rupture is crucial in diagnosis and treatment. Recent studies link the existence of vortices in the blood flow pattern to aneurysm rupture and report observations of embedded vortices - a larger vortex encloses a smaller one flowing in the opposite direction - whose implications are unclear. We present a clustering-based approach for the visual analysis of vortical flow in simulated cerebral aneurysm hemodynamics. We show how embedded vortices develop at saddle-node bifurcations on vortex core lines and convey the participating flow at full manifestation of the vortex by a fast and smart grouping of streamlines and the visualization of group representatives. The grouping result may be refined based on spectral clustering generating a more detailed visualization of the flow pattern, especially further off the core lines. We aim at supporting CFD engineers researching the biological implications of embedded vortices.
Correlation analysis in multidimensional multivariate time-varying datasets
Authors: Najmeh Abedzadeh
Published at SciVis, 2015

One of the most vital challenges for weather forecasters is the correlation between two geographical phenomena that are distributed continuously in multidimensional multivariate time-varying datasets. In this research, we have visualized the correlation between Pressure and Temperature in the climate datasets. Pearson correlation is used in this study to measure the major linear relationship between two variables in the dataset. Using glyphs in the spatial location, we highlighted the significant association between variables. Based on the positive or negative slope of correlation lines, we can conclude how much they are correlated. The principal of this research is visualizing the local trend of variables versus each other in multidimensional multivariate time-varying datasets, which needs to be visualized with their spatial locations in meteorological datasets. Using glyphs, not only can we visualize the correlation between two variables in the coordinate system, but we can also discern whether any of these variables is separately increasing or decreasing. Moreover, we can visualize the background color as another variable and see the correlation lines around of a particular zone such as storm area.
CPU Ray Tracing Large Particle Data with Balanced P-k-d Trees
Authors: Ingo Wald, Aaron Knoll, Gregory P. Johnson, Will Usher 0001, Valerio Pascucci, Michael E. Papka
Published at SciVis, 2015

We present a novel approach to rendering large particle data sets from molecular dynamics, astrophysics and other sources. We employ a new data structure adapted from the original balanced k-d tree, which allows for representation of data with trivial or no overhead. In the OSPRay visualization framework, we have developed an efficient CPU algorithm for traversing, classifying and ray tracing these data. Our approach is able to render up to billions of particles on a typical workstation, purely on the CPU, without any approximations or level-of-detail techniques, and optionally with attribute-based color mapping, dynamic range query, and advanced lighting models such as ambient occlusion and path tracing.
Diderot: a Domain-Specific Language for Portable Parallel Scientific Visualization and Image Analysis
Authors: Gordon L. Kindlmann, Charisee Chiw, Nicholas Seltzer, Lamont Samuels, John H. Reppy
Published at SciVis, 2015

Many algorithms for scientific visualization and image analysis are rooted in the world of continuous scalar, vector, and tensor fields, but are programmed in low-level languages and libraries that obscure their mathematical foundations. Diderot is a parallel domain-specific language that is designed to bridge this semantic gap by providing the programmer with a high-level, mathematical programming notation that allows direct expression of mathematical concepts in code. Furthermore, Diderot provides parallel performance that takes advantage of modern multicore processors and GPUs. The high-level notation allows a concise and natural expression of the algorithms and the parallelism allows efficient execution on real-world datasets.
Distribution Driven Extraction and Tracking of Features for Time-varying Data Analysis
Authors: Soumya Dutta, Han-Wei Shen
Published at SciVis, 2015

Effective analysis of features in time-varying data is essential in numerous scientific applications. Feature extraction and tracking are two important tasks scientists rely upon to get insights about the dynamic nature of the large scale time-varying data. However, often the complexity of the scientific phenomena only allows scientists to vaguely define their feature of interest. Furthermore, such features can have varying motion patterns and dynamic evolution over time. As a result, automatic extraction and tracking of features becomes a non-trivial task. In this work, we investigate these issues and propose a distribution driven approach which allows us to construct novel algorithms for reliable feature extraction and tracking with high confidence in the absence of accurate feature definition. We exploit two key properties of an object, motion and similarity to the target feature, and fuse the information gained from them to generate a robust feature-aware classification field at every time step. Tracking of features is done using such classified fields which enhances the accuracy and robustness of the proposed algorithm. The efficacy of our method is demonstrated by successfully applying it on several scientific data sets containing a wide range of dynamic time-varying features.
Effective Visualization of Temporal Ensembles
Authors: Lihua Hao, Christopher G. Healey, Steffen A. Bass
Published at SciVis, 2015

An ensemble is a collection of related datasets, called members, built from a series of runs of a simulation or an experiment. Ensembles are large, temporal, multidimensional, and multivariate, making them difficult to analyze. Another important challenge is visualizing ensembles that vary both in space and time. Initial visualization techniques displayed ensembles with a small number of members, or presented an overview of an entire ensemble, but without potentially important details. Recently, researchers have suggested combining these two directions, allowing users to choose subsets of members to visualization. This manual selection process places the burden on the user to identify which members to explore. We first introduce a static ensemble visualization system that automatically helps users locate interesting subsets of members to visualize. We next extend the system to support analysis and visualization of temporal ensembles. We employ 3D shape comparison, cluster tree visualization, and glyph based visualization to represent different levels of detail within an ensemble. This strategy is used to provide two approaches for temporal ensemble analysis: (1) segment based ensemble analysis, to capture important shape transition time-steps, clusters groups of similar members, and identify common shape changes over time across multiple members; and (2) time-step based ensemble analysis, which assumes ensemble members are aligned in time by combining similar shapes at common time-steps. Both approaches enable users to interactively visualize and analyze a temporal ensemble from different perspectives at different levels of detail. We demonstrate our techniques on an ensemble studying matter transition from hadronic gas to quark-gluon plasma during gold-on-gold particle collisions.
Effectiveness of Structured Textures on Dynamically Changing Terrain-like Surfaces
Authors: Thomas Butkiewicz, Andrew H. Stevens
Published at SciVis, 2015

Previous perceptual research and human factors studies have identified several effective methods for texturing 3D surfaces to ensure that their curvature is accurately perceived by viewers. However, most of these studies examined the application of these techniques to static surfaces. This paper explores the effectiveness of applying these techniques to dynamically changing surfaces. When these surfaces change shape, common texturing methods, such as grids and contours, induce a range of different motion cues, which can draw attention and provide information about the size, shape, and rate of change. A human factors study was conducted to evaluate the relative effectiveness of these methods when applied to dynamically changing pseudo-terrain surfaces. The results indicate that, while no technique is most effective for all cases, contour lines generally perform best, and that the pseudo-contour lines induced by banded color scales convey the same benefits.
Explicit Frequency Control for High-Quality Texture-Based Flow Visualization
Authors: Victor Matvienko, Jens H. Krüger
Published at SciVis, 2015

In this work we propose an effective method for frequency-controlled dense flow visualization derived from a generalization of the Line Integral Convolution (LIC) technique. Our approach consists in considering the spectral properties of the dense flow visualization process as an integral operator defined in a local curvilinear coordinate system aligned with the flow. Exploring LIC from this point of view, we suggest a systematic way to design a flow visualization process with particular local spatial frequency properties of the resulting image. Our method is efficient, intuitive, and based on a long-standing model developed as a result of numerous perception studies. The method can be described as an iterative application of line integral convolution, followed by a one-dimensional Gabor filtering orthogonal to the flow. To demonstrate the utility of the technique, we generated novel adaptive multi-frequency flow visualizations, that according to our evaluation, feature a higher level of frequency control and higher quality scores than traditional approaches in texture-based flow visualization.
Extracting, Tracking, and Visualizing Magnetic Flux Vortices in 3D Complex-Valued Superconductor Simulation Data
Authors: Hanqi Guo 0001, Carolyn L. Phillips, Tom Peterka, Dmitry A. Karpeyev, Andreas Glatz
Published at SciVis, 2015

We propose a method for the vortex extraction and tracking of superconducting magnetic flux vortices for both structured and unstructured mesh data. In the Ginzburg-Landau theory, magnetic flux vortices are well-defined features in a complex-valued order parameter field, and their dynamics determine electromagnetic properties in type-II superconductors. Our method represents each vortex line (a 1D curve embedded in 3D space) as a connected graph extracted from the discretized field in both space and time. For a time-varying discrete dataset, our vortex extraction and tracking method is as accurate as the data discretization. We then apply 3D visualization and 2D event diagrams to the extraction and tracking results to help scientists understand vortex dynamics and macroscale superconductor behavior in greater detail than previously possible.
Feature-Based Tensor Field Visualization for Fiber Reinforced Polymers
Authors: Valentin Zobel, Markus Stommel, Gerik Scheuermann
Published at SciVis, 2015

Virtual testing is an integral part of modern product development in mechanical engineering. Numerical structure simulations allow the computation of local stresses which are given as tensor fields. For homogeneous materials, the tensor information is usually reduced to a scalar field like the von Mises stress. A material-dependent threshold defines the material failure answering the key question of engineers. This leads to a rather simple feature-based visualisation. For composite materials like short fiber reinforced polymers, the situation is much more complex. The material property is determined by the fiber distribution at every position, often described as fiber orientation tensor field. Essentially, the material's ability to cope with stress becomes anisotropic and inhomogeneous. We show how to combine the stress field and the fiber orientation field in such cases, leading to a feature-based visualization of tensor fields for composite materials. The resulting features inform the engineer about potential improvements in the product development.
Gaze Stripes: Image-Based Visualization of Eye Tracking Data
Authors: Kuno Kurzhals, Marcel Hlawatsch, Florian Heimerl, Michael Burch, Thomas Ertl, Daniel Weiskopf
Published at SciVis, 2015

We present a new visualization approach for displaying eye tracking data from multiple participants. We aim to show the spatio-temporal data of the gaze points in the context of the underlying image or video stimulus without occlusion. Our technique, denoted as gaze stripes, does not require the explicit definition of areas of interest but directly uses the image data around the gaze points, similar to thumbnails for images. A gaze stripe consists of a sequence of such gaze point images, oriented along a horizontal timeline. By displaying multiple aligned gaze stripes, it is possible to analyze and compare the viewing behavior of the participants over time. Since the analysis is carried out directly on the image data, expensive post-processing or manual annotation are not required. Therefore, not only patterns and outliers in the participants' scanpaths can be detected, but the context of the stimulus is available as well. Furthermore, our approach is especially well suited for dynamic stimuli due to the non-aggregated temporal mapping. Complementary views, i.e., markers, notes, screenshots, histograms, and results from automatic clustering, can be added to the visualization to display analysis results. We illustrate the usefulness of our technique on static and dynamic stimuli. Furthermore, we discuss the limitations and scalability of our approach in comparison to established visualization techniques.
Glyph-Based Comparative Visualization for Diffusion Tensor Fields
Authors: Changgong Zhang, Thomas Schultz 0001, Kai Lawonn, Elmar Eisemann, Anna Vilanova
Published at SciVis, 2015

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.
High performance flow field visualization with high-order access dependencies
Authors: Jiang Zhang 0002, Hanqi Guo 0001, Xiaoru Yuan
Published at SciVis, 2015

We present a novel model based on high-order access dependencies for high performance pathline computation in flow field. The high-order access dependencies are defined as transition probabilities from one data block to other blocks based on a few historical data accesses. Compared with existing methods which employed first-order access dependencies, our approach takes the advantages of high order access dependencies with higher accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing densely-seeded pathlines. The efficiency of our proposed approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method can achieve higher data locality than the first-order access dependencies based method, thereby reducing the I/O requests and improving the efficiency of pathline computation in various applications.