Showing papers by "David H. Laidlaw published in 2005"

Comparing 2D vector field visualization methods: a user study

Abstract: We present results from a user study that compared six visualization methods for two-dimensional vector data. Users performed three simple but representative tasks using visualizations from each method: 1) locating all critical points in an image, 2) identifying critical point types, and 3) advecting a particle. Visualization methods included two that used different spatial distributions of short arrow icons, two that used different distributions of integral curves, one that used wedges located to suggest flow lines, and line-integral convolution (LIC). Results show different strengths and weaknesses for each method. We found that users performed these tasks better with methods that: 1) showed the sign of vectors within the vector field, 2) visually represented integral curves, and 3) visually represented the locations of critical points. Expert user performance was not statistically different from nonexpert user performance. We used several methods to analyze the data including omnibus analysis of variance, pairwise t-tests, and graphical analysis using inferential confidence intervals. We concluded that using the inferential confidence intervals for displaying the overall pattern of results for each task measure and for performing subsequent pairwise comparisons of the condition means was the best method for analyzing the data in this study. These results provide quantitative support for some of the anecdotal evidence concerning visualization methods. The tasks and testing framework also provide a basis for comparing other visualization methods, for creating more effective methods and for defining additional tasks to further understand the tradeoffs among the methods. In the future, we also envision extending this work to more ambitious comparisons, such as evaluating two-dimensional vectors on two-dimensional surfaces embedded in three-dimensional space and defining analogous tasks for three-dimensional visualization methods.

Artistic collaboration in designing VR visualizations

Abstract: This article describes one of the recent major collaborative efforts, a class on designing VR scientific visualizations that was co-taught with professors and students from Brown University's computer science department and the Rhode Island School of Design's (RISD's) illustration department. We discuss here the experiences that led us to this conclusion; along with some of the tools we have developed to facilitate working with artists. Many of the experiences and conclusions relayed here are the results of this class. We then discuss three important themes that we derived from our experiences, which are all motivated by a desire to better facilitate artistic collaborations.

Anatomical analysis of an aye-aye brain (Daubentonia madagascariensis, primates: Prosimii) combining histology, structural magnetic resonance imaging, and diffusion-tensor imaging.

Abstract: This report presents initial results of a multimodal analysis of tissue volume and microstructure in the brain of an aye-aye (Daubentonia madagascariensis). The left hemisphere of an aye-aye brain was scanned using T2-weighted structural magnetic resonance imaging (MRI) and diffusion-tensor imaging (DTI) prior to histological processing and staining for Nissl substance and myelinated fibers. The objectives of the experiment were to estimate the volume of gross brain regions for comparison with published data on other prosimians and to validate DTI data on fiber anisotropy with histological measurements of fiber spread. Measurements of brain structure volumes in the specimen are consistent with those reported in the literature: the aye-aye has a very large brain for its body size, a reduced volume of visual structures (V1 and LGN), and an increased volume of the olfactory lobe. This trade-off between visual and olfactory reliance is likely a reflection of the nocturnal extractive foraging behavior practiced by Daubentonia. Additionally, frontal cortex volume is large in the aye-aye, a feature that may also be related to its complex foraging behavior and sensorimotor demands. Analysis of DTI data in the anterior cingulum bundle demonstrates a strong correlation between fiber spread as measured from histological sections and fiber spread as measured from DTI. These results represent the first quantitative comparison of DTI data and fiber-stained histology in the brain.

16 – Diffusion Tensor MRI Visualization

Abstract: Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is emerging as an important technology for elucidating the internal structure of the brain and for diagnosing conditions affecting the integrity of nervous tissue. Since DTI technology emerged 10 years ago, the problems inherent in DTI acquisition, visualization, analysis, and application have spurred numerous multidisciplinary efforts. For scientific visualization students, these problems are especially intriguing because DTIs are large 3D multivariate datasets and thus present many visualization challenges. On the other hand, these problems have real-world origins, applications, and challenges: The datasets are noisy, resolution is never sufficient, and partial-volume effects limit the results. Perhaps the greatest challenge is the breadth of knowledge necessary to truly understand the entire process: from patient to imaging to computation to visual analysis and back to patient. This chapter gives a brief survey of the past 10 years of visualization-related research, including some of the outstanding issues.

Simulation and visualization of air flow around bat wings during flight

Abstract: This paper presents a case study of interdisciplinary collaboration in building a set of tools to simulate and visualize airflow around bat wings during flight. A motion capture system is used to generate 3D coordinates of infrared markers attached to the wings of a bat flying in a wind tunnel. Marker positions that cannot be determined due to high wing deformation are reconstructed on the basis of the proper orthogonal decomposition (POD). The geometry obtained for the wings is used to generate a sequence of unstructured tetrahedral meshes. The incompressible Navier-Stokes equations in arbitrary Lagrangian-Eulerian formulation are solved using the hybrid spectral/hp element solver Nektar. Preliminary simulation results are visualized in the CAVE, an immersive, 3D, stereo display environment.

Design and evaluation in visualization research

Abstract: This panel brings together researchers who have been pioneering quite different approaches to visualization research by integrating evaluation and knowledge of visual design into their work. The panelists will present their views and experiences in using user studies for quantitative evaluation of methods, in integrating the expertise of visually trained designers into the development of methods, and in exploring the parameter space of visualization possibilities using "human-in-the-loop" experiments. A goal of the panel is to encourage a lively and stimulating discussion by presenting challenging but highly contrasting ideas. The panel will follow the usual pattern of short position presentations, taking care to leave ample time for audience interaction, questions, and comments.

Painting and Visualization.

Abstract: Scientific visualization applications can be divided into two categories: expository and exploratory. This chapter focuses on exploratory applications. Exploratory applications typically represent complicated scientific data as fully as possible so that a scientific user can interactively explore it. As per the scientific method, a scientist gathers data to test a hypothesis, but the binary answer to that test is usually just a beginning. The challenge comes in understanding the correlations and dependencies among all the values. The chapter begins with a narrative of some of the work in the area of representing multivalued data, illustrating more specifically some of the ways in which art can be brought to bear on scientific visualization. The chapter then gives a broader survey of scientific visualization work that has been influenced by art, followed by a discussion of some of the open issues in this area, which ties back to studying art, design, and art education.

Characterizing the Effect of Level of Immersion on a 3D Marking Task

Abstract: We present a user study comparing multiple immersive environments for the performance of a task. We expected them to perform differently but it was not obvious how they would differ. Our analysis aimed to discover the environment in which subjects performed best and the environment they preferred. The task, derived from developmental biology, requires a user interactively to label spheres distributed in a volume by marking the spheres in 3D. We tested three virtual reality displays: a CAVE-like environment, a single-wall display, and a desktop system (fish tank virtual reality). All displays supported head-tracked stereo viewing and a tracked wand device. The task conditions varied in field of view, apparent size of the spheres, and whether the user was standing or sitting. We also varied the number of targets that had already been marked when the user began a trial. Popular user interaction techniques allow the user to change viewpoint and do the marking. Data we collected led to four significant findings: a) in the Cave the subjects preferred medium sized or large spheres over small spheres, b) when only a few of the targets have been marked, larger spheres were marked faster than small spheres, c) large spheres are marked most accurately, and d) our wall display is not comparable to our fish tank virtual reality environment when the spheres are small. Additionally, occlusion and larger field of view inhibited performance in the Cave more than at the fish tank when the task was dominated by visual search. (a) (b) (c) Figure 1: Marking spheres in different virtual environments: (a) at the fish tank, (b) at the single wall, and (c) in the Cave.

Using Visual Design Expertise to Characterize the Effectiveness of 2D Scientific Visualization Methods.

Abstract: Figure 1: Eleven different visualization methods that represent the same continuous scalar dataset. We are characterizing the effectiveness of each one of these methods, both individually and in combination, to represent scalar datasets in 2D. We present the results from a pilot study that evaluates the effectiveness of 2D visualization methods in terms of a set of design factors, which are subjectively rated by expert visual designers. In collaboration with educators from the Illustration Department at the Rhode Island School of Design (RISD), we have defined a space of visualization methods using basic visual elements including icon hue, icon size, icon density, and background saturation (see Figure 1). In this initial pilot study we presented our subjects with single variable visualization methods. The results characterize the effectiveness of individual visual elements according to our design factors. We are beginning to test these results by creating two-variable visualizations and studying how the different visual elements interact. 1 INTRODUCTION Given the increasing capacity of scientists to acquire or calculate multival-ued datasets, creating effective visualizations for understanding and correlating these data is imperative. However, modeling the space of possible vi-sualization methods for a given scientific problem has challenged computer scientists, statisticians, and cognitive scientists for many years [1,2,3,4]; it is still an open challenge. Our goal is to provide scientists with visualization methods that convey information by optimizing the design of the images to facilitate perception and comprehension. We created a framework for evaluating these visualization methods through feedback from expert visual designers and art educators. Our framework mimics the art education process, in which art educators impart artistic and visual design knowledge to their students through critiques of the students' work.We established a set of factors that characterize the effectiveness of a visualization method in displaying scientific data. These factors include constraints implied by the dataset, such as the relative importance of the different data variables or the minimum feature size present in the data. We also include design, artistic, and perceptual factors, such as time required to understand the visualization, or how visually linear is the mapping between data and visual element across the image. We will describe these in detail in section 2. Evaluating the effectiveness of visualizations is difficult because tests to evaluate them meaningfully are hard to design and execute [5]. We have researched this issue previously in two user studies comparing 2D vector visualization methods. The first …

A haptic interface for creating smooth 3D curves with varying line weight

Abstract: We present a two-handed haptic interface for free-form 3D modeling in virtual reality that provides the artist with the ability to input controlled, smooth, 3D curves and to naturally vary line weight while drawing a 3D mark. As free-form modeling tools are used to tackle more and more complicated subjects, such as scientific illustrations [Keefe et al. 2005], artists are finding that they need more control over their modeling tools. This interface is an attempt to provide artists with more control in specifying both 3D form and style. The modeling medium we use for this project is something like virtual wire sculpture: 3D models made of thin ribbon-like curves. These curves are typically viewed with a stereo virtual reality display and they exist in a three-dimensional space, so they can be viewed from any direction (three views of an example model are shown in Figure 1).