DevLan/vispubs huggingface dataset:
Real-time Uncertainty Visualization for B-Mode Ultrasound
Authors: Christian Schulte zu Berge, Denis Declara, Christoph Hennersperger, Maximilian Baust, Nassir Navab
Published at SciVis, 2015

B-mode ultrasound is a very well established imaging modality and is widely used in many of today's clinical routines. However, acquiring good images and interpreting them correctly is a challenging task due to the complex ultrasound image formation process depending on a large number of parameters. To facilitate ultrasound acquisitions, we introduce a novel framework for real-time uncertainty visualization in B-mode images. We compute real-time per-pixel ultrasound Confidence Maps, which we fuse with the original ultrasound image in order to provide the user with an interactive feedback on the quality and credibility of the image. In addition to a standard color overlay mode, primarily intended for educational purposes, we propose two perceptional visualization schemes to be used in clinical practice. Our mapping of uncertainty to chroma uses the perceptionally uniform L*a*b* color space to ensure that the perceived brightness of B-mode ultrasound remains the same. The alternative mapping of uncertainty to fuzziness keeps the B-mode image in its original grayscale domain and locally blurs or sharpens the image based on the uncertainty distribution. An elaborate evaluation of our system and user studies on both medical students and expert sonographers demonstrate the usefulness of our proposed technique. In particular for ultrasound novices, such as medical students, our technique yields powerful visual cues to evaluate the image quality and thereby learn the ultrasound image formation process. Furthermore, seeing the distribution of uncertainty adjust to the transducer positioning in real-time, provides also expert clinicians with a strong visual feedback on their actions. This helps them to optimize the acoustic window and can improve the general clinical value of ultrasound.
Reconstruction and Visualization of Coordinated 3D Cell Migration Based on Optical Flow
Authors: Christopher P. Kappe, Lucas Schutz, Stefan Gunther, Lars Hufnagel, Steffen Lemke, Heike Leitte
Published at SciVis, 2015

Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this reorganization is coordinated. While modern microscopes can record video data for every cell during animal development in 3D+t, analyzing these videos remains a major challenge: reconstruction of comprehensive cell tracks turned out to be very demanding especially with decreasing data quality and increasing cell densities. In this paper, we present an analysis pipeline for coordinated cellular motions in developing embryos based on the optical flow of a series of 3D images. We use numerical integration to reconstruct cellular long-term motions in the optical flow of the video, we take care of data validation, and we derive a LIC-based, dense flow visualization for the resulting pathlines. This approach allows us to handle low video quality such as noisy data or poorly separated cells, and it allows the biologists to get a comprehensive understanding of their data by capturing dynamic growth processes in stills. We validate our methods using three videos of growing fruit fly embryos.
Rotation Invariant Vortices for Flow Visualization
Authors: Tobias Günther, Maik Schulze, Holger Theisel
Published at SciVis, 2015

We propose a new class of vortex definitions for flows that are induced by rotating mechanical parts, such as stirring devices, helicopters, hydrocyclones, centrifugal pumps, or ventilators. Instead of a Galilean invariance, we enforce a rotation invariance, i.e., the invariance of a vortex under a uniform-speed rotation of the underlying coordinate system around a fixed axis. We provide a general approach to transform a Galilean invariant vortex concept to a rotation invariant one by simply adding a closed form matrix to the Jacobian. In particular, we present rotation invariant versions of the well-known Sujudi-Haimes, Lambda-2, and Q vortex criteria. We apply them to a number of artificial and real rotating flows, showing that for these cases rotation invariant vortices give better results than their Galilean invariant counterparts.
Streamline Variability Plots for Characterizing the Uncertainty in Vector Field Ensembles
Authors: Florian Ferstl, Kai Bürger, Rüdiger Westermann
Published at SciVis, 2015

We present a new method to visualize from an ensemble of flow fields the statistical properties of streamlines passing through a selected location. We use principal component analysis to transform the set of streamlines into a low-dimensional Euclidean space. In this space the streamlines are clustered into major trends, and each cluster is in turn approximated by a multivariate Gaussian distribution. This yields a probabilistic mixture model for the streamline distribution, from which confidence regions can be derived in which the streamlines are most likely to reside. This is achieved by transforming the Gaussian random distributions from the low-dimensional Euclidean space into a streamline distribution that follows the statistical model, and by visualizing confidence regions in this distribution via iso-contours. We further make use of the principal component representation to introduce a new concept of streamline-median, based on existing median concepts in multidimensional Euclidean spaces. We demonstrate the potential of our method in a number of real-world examples, and we compare our results to alternative clustering approaches for particle trajectories as well as curve boxplots.
TelCoVis: Visual Exploration of Co-occurrence in Urban Human Mobility Based on Telco Data
Authors: Wenchao Wu, Jiayi Xu 0001, Haipeng Zeng, Yixian Zheng, Huamin Qu, Bing Ni, Mingxuan Yuan, Lionel M. Ni
Published at SciVis, 2015

Understanding co-occurrence in urban human mobility (i.e. people from two regions visit an urban place during the same time span) is of great value in a variety of applications, such as urban planning, business intelligence, social behavior analysis, as well as containing contagious diseases. In recent years, the widespread use of mobile phones brings an unprecedented opportunity to capture large-scale and fine-grained data to study co-occurrence in human mobility. However, due to the lack of systematic and efficient methods, it is challenging for analysts to carry out in-depth analyses and extract valuable information. In this paper, we present TelCoVis, an interactive visual analytics system, which helps analysts leverage their domain knowledge to gain insight into the co-occurrence in urban human mobility based on telco data. Our system integrates visualization techniques with new designs and combines them in a novel way to enhance analysts' perception for a comprehensive exploration. In addition, we propose to study the correlations in co-occurrence (i.e. people from multiple regions visit different places during the same time span) by means of biclustering techniques that allow analysts to better explore coordinated relationships among different regions and identify interesting patterns. The case studies based on a real-world dataset and interviews with domain experts have demonstrated the effectiveness of our system in gaining insights into co-occurrence and facilitating various analytical tasks.
Using Maximum Topology Matching to Explore Differences in Species Distribution Models
Authors: Jorge Poco, Harish Doraiswamy, Marian Talbert, Jeffrey T. Morisette, Cláudio T. Silva
Published at SciVis, 2015

Species distribution models (SDM) are used to help understand what drives the distribution of various plant and animal species. These models are typically high dimensional scalar functions, where the dimensions of the domain correspond to predictor variables of the model algorithm. Understanding and exploring the differences between models help ecologists understand areas where their data or understanding of the system is incomplete and will help guide further investigation in these regions. These differences can also indicate an important source of model to model uncertainty. However, it is cumbersome and often impractical to perform this analysis using existing tools, which allows for manual exploration of the models usually as 1-dimensional curves. In this paper, we propose a topology-based framework to help ecologists explore the differences in various SDMs directly in the high dimensional domain. In order to accomplish this, we introduce the concept of maximum topology matching that computes a locality-aware correspondence between similar extrema of two scalar functions. The matching is then used to compute the similarity between two functions. We also design a visualization interface that allows ecologists to explore SDMs using their topological features and to study the differences between pairs of models found using maximum topological matching. We demonstrate the utility of the proposed framework through several use cases using different data sets and report the feedback obtained from ecologists.
Visual Verification of Space Weather Ensemble Simulations
Authors: Alexander Bock 0002, Asher Pembroke, M. Leila Mays, Lutz Rastaetter, Timo Ropinski, Anders Ynnerman
Published at SciVis, 2015

We propose a system to analyze and contextualize simulations of coronal mass ejections. As current simulation techniques require manual input, uncertainty is introduced into the simulation pipeline leading to inaccurate predictions that can be mitigated through ensemble simulations. We provide the space weather analyst with a multi-view system providing visualizations to: 1. compare ensemble members against ground truth measurements, 2. inspect time-dependent information derived from optical flow analysis of satellite images, and 3. combine satellite images with a volumetric rendering of the simulations. This three-tier workflow provides experts with tools to discover correlations between errors in predictions and simulation parameters, thus increasing knowledge about the evolution and propagation of coronal mass ejections that pose a danger to Earth and interplanetary travel.
Visualization and Analysis of Rotating Stall for Transonic Jet Engine Simulation
Authors: Chun-Ming Chen, Soumya Dutta, Xiaotong Liu, Gregory Heinlein, Han-Wei Shen, Jen-Ping Chen
Published at SciVis, 2015

Identification of early signs of rotating stall is essential for the study of turbine engine stability. With recent advancements of high performance computing, high-resolution unsteady flow fields allow in depth exploration of rotating stall and its possible causes. Performing stall analysis, however, involves significant effort to process large amounts of simulation data, especially when investigating abnormalities across many time steps. In order to assist scientists during the exploration process, we present a visual analytics framework to identify suspected spatiotemporal regions through a comparative visualization so that scientists are able to focus on relevant data in more detail. To achieve this, we propose efficient stall analysis algorithms derived from domain knowledge and convey the analysis results through juxtaposed interactive plots. Using our integrated visualization system, scientists can visually investigate the detected regions for potential stall initiation and further explore these regions to enhance the understanding of this phenomenon. Positive feedback from scientists demonstrate the efficacy of our system in analyzing rotating stall.
Visualization-by-Sketching: An Artist's Interface for Creating Multivariate Time-Varying Data Visualizations
Authors: David Schroeder, Daniel F. Keefe
Published at SciVis, 2015

We present Visualization-by-Sketching, a direct-manipulation user interface for designing new data visualizations. The goals are twofold: First, make the process of creating real, animated, data-driven visualizations of complex information more accessible to artists, graphic designers, and other visual experts with traditional, non-technical training. Second, support and enhance the role of human creativity in visualization design, enabling visual experimentation and workflows similar to what is possible with traditional artistic media. The approach is to conceive of visualization design as a combination of processes that are already closely linked with visual creativity: sketching, digital painting, image editing, and reacting to exemplars. Rather than studying and tweaking low-level algorithms and their parameters, designers create new visualizations by painting directly on top of a digital data canvas, sketching data glyphs, and arranging and blending together multiple layers of animated 2D graphics. This requires new algorithms and techniques to interpret painterly user input relative to data “under” the canvas, balance artistic freedom with the need to produce accurate data visualizations, and interactively explore large (e.g., terabyte-sized) multivariate datasets. Results demonstrate a variety of multivariate data visualization techniques can be rapidly recreated using the interface. More importantly, results and feedback from artists support the potential for interfaces in this style to attract new, creative users to the challenging task of designing more effective data visualizations and to help these users stay “in the creative zone” as they work.
Visualizing 3D flow through cutting planes
Authors: Colin Ware, Andrew H. Stevens
Published at SciVis, 2015

Studies have found conflicting results regarding the effectiveness of tube-like structures for representing 3D flow data. This paper presents the findings of a small-scale pilot study contrasting static monoscopic depth cues to ascertain their importance in perceiving the orientation of a three-dimensional glyph with respect to a cutting plane. A simple striped texture and shading were found to reduce judgement errors when used with a 3D tube glyph as compared to plain or shaded line glyphs. A discussion of considerations for a full-scale study and possible future work follows.
Visualizing crossing probabilistic tracts
Authors: Mathias Goldau, André Reichenbach, Mario Hlawitschka
Published at SciVis, 2015

Diffusion weighted magnetic resonance imaging (dMRI) together with tractography algorithms allow to probe for principal white matter tracts in the living human brain. Specifically, probabilistic tractography quantifies the existence of physical connections to a given seed region as a 3D scalar map of confidence scores. Fiber-Stippling is a visualization for probabilistic tracts that effectively communicates the diffusion pattern, connectivity score, and anatomical context. Unfortunately, it cannot handle multiple diffusion orientations per voxel, which exist in high angular resolution diffusion imaging (HARDI) data. Such data is needed to resolve tracts in complex configurations, such as crossings. In this work, we suggest a visualization based on Fiber-Stippling but sensible to multiple diffusion orientations from HARDI-based diffusion models. With such a technique, it is now possible to visualize probabilistic tracts from HARDI-based tractography algorithms. This implies that tract crossings may now be visualized as crossing stipples, which is an essential step towards an accurate visualization of the neuroanatomy, as crossing tracts are widespread phenomena in the brain.
Visualizing Tensor Normal Distributions at Multiple Levels of Detail
Authors: Amin Abbasloo, Vitalis Wiens, Max Hermann, Thomas Schultz 0001
Published at SciVis, 2015

Despite the widely recognized importance of symmetric second order tensor fields in medicine and engineering, the visualization of data uncertainty in tensor fields is still in its infancy. A recently proposed tensorial normal distribution, involving a fourth order covariance tensor, provides a mathematical description of how different aspects of the tensor field, such as trace, anisotropy, or orientation, vary and covary at each point. However, this wealth of information is far too rich for a human analyst to take in at a single glance, and no suitable visualization tools are available. We propose a novel approach that facilitates visual analysis of tensor covariance at multiple levels of detail. We start with a visual abstraction that uses slice views and direct volume rendering to indicate large-scale changes in the covariance structure, and locations with high overall variance. We then provide tools for interactive exploration, making it possible to drill down into different types of variability, such as in shape or orientation. Finally, we allow the analyst to focus on specific locations of the field, and provide tensor glyph animations and overlays that intuitively depict confidence intervals at those points. Our system is demonstrated by investigating the effects of measurement noise on diffusion tensor MRI, and by analyzing two ensembles of stress tensor fields from solid mechanics.
A comparative study between RadViz and Star Coordinates
Authors: Manuel Rubio-Sánchez, Laura Raya, Francisco Diaz, Alberto Sánchez 0001
Published at InfoVis, 2015

RadViz and star coordinates are two of the most popular projection-based multivariate visualization techniques that arrange variables in radial layouts. Formally, the main difference between them consists of a nonlinear normalization step inherent in RadViz. In this paper we show that, although RadViz can be useful when analyzing sparse data, in general this design choice limits its applicability and introduces several drawbacks for exploratory data analysis. In particular, we observe that the normalization step introduces nonlinear distortions, can encumber outlier detection, prevents associating the plots with useful linear mappings, and impedes estimating original data attributes accurately. In addition, users have greater flexibility when choosing different layouts and views of the data in star coordinates. Therefore, we suggest that analysts and researchers should carefully consider whether RadViz's normalization step is beneficial regarding the data sets' characteristics and analysis tasks.
A Linguistic Approach to Categorical Color Assignment for Data Visualization
Authors: Vidya Setlur, Maureen C. Stone
Published at InfoVis, 2015

When data categories have strong color associations, it is useful to use these semantically meaningful concept-color associations in data visualizations. In this paper, we explore how linguistic information about the terms defining the data can be used to generate semantically meaningful colors. To do this effectively, we need first to establish that a term has a strong semantic color association, then discover which color or colors express it. Using co-occurrence measures of color name frequencies from Google n-grams, we define a measure for colorability that describes how strongly associated a given term is to any of a set of basic color terms. We then show how this colorability score can be used with additional semantic analysis to rank and retrieve a representative color from Google Images. Alternatively, we use symbolic relationships defined by WordNet to select identity colors for categories such as countries or brands. To create visually distinct color palettes, we use k-means clustering to create visually distinct sets, iteratively reassigning terms with multiple basic color associations as needed. This can be additionally constrained to use colors only in a predefined palette.
A Psychophysical Investigation of Size as a Physical Variable
Authors: Yvonne Jansen, Kasper Hornbæk
Published at InfoVis, 2015

Physical visualizations, or data physicalizations, encode data in attributes of physical shapes. Despite a considerable body of work on visual variables, “physical variables” remain poorly understood. One of them is physical size. A difficulty for solid elements is that “size” is ambiguous - it can refer to either length/diameter, surface, or volume. Thus, it is unclear for designers of physicalizations how to effectively encode quantities in physical size. To investigate, we ran an experiment where participants estimated ratios between quantities represented by solid bars and spheres. Our results suggest that solid bars are compared based on their length, consistent with previous findings for 2D and 3D bars on flat media. But for spheres, participants' estimates are rather proportional to their surface. Depending on the estimation method used, judgments are rather consistent across participants, thus the use of perceptually-optimized size scales seems possible. We conclude by discussing implications for the design of data physicalizations and the need for more empirical studies on physical variables.
A Simple Approach for Boundary Improvement of Euler Diagrams
Authors: Paolo Simonetto, Daniel Archambault, Carlos Scheidegger
Published at InfoVis, 2015

General methods for drawing Euler diagrams tend to generate irregular polygons. Yet, empirical evidence indicates that smoother contours make these diagrams easier to read. In this paper, we present a simple method to smooth the boundaries of any Euler diagram drawing. When refining the diagram, the method must ensure that set elements remain inside their appropriate boundaries and that no region is removed or created in the diagram. Our approach uses a force system that improves the diagram while at the same time ensuring its topological structure does not change. We demonstrate the effectiveness of the approach through case studies and quantitative evaluations.
Acquired Codes of Meaning in Data Visualization and Infographics: Beyond Perceptual Primitives
Authors: Lydia Byrne, Daniel Angus, Janet Wiles
Published at InfoVis, 2015

While information visualization frameworks and heuristics have traditionally been reluctant to include acquired codes of meaning, designers are making use of them in a wide variety of ways. Acquired codes leverage a user's experience to understand the meaning of a visualization. They range from figurative visualizations which rely on the reader's recognition of shapes, to conventional arrangements of graphic elements which represent particular subjects. In this study, we used content analysis to codify acquired meaning in visualization. We applied the content analysis to a set of infographics and data visualizations which are exemplars of innovative and effective design. 88% of the infographics and 71% of data visualizations in the sample contain at least one use of figurative visualization. Conventions on the arrangement of graphics are also widespread in the sample. In particular, a comparison of representations of time and other quantitative data showed that conventions can be specific to a subject. These results suggest that there is a need for information visualization research to expand its scope beyond perceptual channels, to include social and culturally constructed meaning. Our paper demonstrates a viable method for identifying figurative techniques and graphic conventions and integrating them into heuristics for visualization design.