Ways of Seeing Data: a Survey of Fields of Visualization
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Scientific Visualization
Report from Dagstuhl Seminar 11231 Scientific Visualization Edited by Min Chen1, Hans Hagen2, Charles D. Hansen3, and Arie Kaufman4 1 University of Oxford, GB, [email protected] 2 TU Kaiserslautern, DE, [email protected] 3 University of Utah, US, [email protected] 4 SUNY – Stony Brook, US, [email protected] Abstract This report documents the program and the outcomes of Dagstuhl Seminar 11231 “Scientific Visualization”. Seminar 05.–10. June, 2011 – www.dagstuhl.de/11231 1998 ACM Subject Classification I.3 Computer Graphics, I.4 Image Processing and Computer Vision, J.2 Physical Sciences and Engineering, J.3 Life and Medical Sciences Keywords and phrases Scientific Visualization, Biomedical Visualization, Integrated Multifield Visualization, Uncertainty Visualization, Scalable Visualization Digital Object Identifier 10.4230/DagRep.1.6.1 1 Executive Summary Min Chen Hans Hagen Charles D. Hansen Arie Kaufman License Creative Commons BY-NC-ND 3.0 Unported license © Min Chen, Hans Hagen, Charles D. Hansen, and Arie Kaufman Scientific Visualization (SV) is the transformation of abstract data, derived from observation or simulation, into readily comprehensible images, and has proven to play an indispensable part of the scientific discovery process in many fields of contemporary science. This seminar focused on the general field where applications influence basic research questions on one hand while basic research drives applications on the other. Reflecting the heterogeneous structure of Scientific Visualization and the currently unsolved problems in the field, this seminar dealt with key research problems and their solutions in the following subfields of scientific visualization: Biomedical Visualization: Biomedical visualization and imaging refers to the mechan- isms and techniques utilized to create and display images of the human body, organs or their components for clinical or research purposes. -
Point-Based Computer Graphics
Point-Based Computer Graphics Eurographics 2002 Tutorial T6 Organizers Markus Gross ETH Zürich Hanspeter Pfister MERL, Cambridge Presenters Marc Alexa TU Darmstadt Markus Gross ETH Zürich Mark Pauly ETH Zürich Hanspeter Pfister MERL, Cambridge Marc Stamminger Bauhaus-Universität Weimar Matthias Zwicker ETH Zürich Contents Tutorial Schedule................................................................................................2 Presenters Biographies........................................................................................3 Presenters Contact Information ..........................................................................4 References...........................................................................................................5 Project Pages.......................................................................................................6 Tutorial Schedule 8:30-8:45 Introduction (M. Gross) 8:45-9:45 Point Rendering (M. Zwicker) 9:45-10:00 Acquisition of Point-Sampled Geometry and Appearance I (H. Pfister) 10:00-10:30 Coffee Break 10:30-11:15 Acquisition of Point-Sampled Geometry and Appearance II (H. Pfister) 11:15-12:00 Dynamic Point Sampling (M. Stamminger) 12:00-14:00 Lunch 14:00-15:00 Point-Based Surface Representations (M. Alexa) 15:00-15:30 Spectral Processing of Point-Sampled Geometry (M. Gross) 15:30-16:00 Coffee Break 16:00-16:30 Efficient Simplification of Point-Sampled Geometry (M. Pauly) 16:30-17:15 Pointshop3D: An Interactive System for Point-Based Surface Editing (M. Pauly) 17:15-17:30 Discussion (all) 2 Presenters Biographies Dr. Markus Gross is a professor of computer science and the director of the computer graphics laboratory of the Swiss Federal Institute of Technology (ETH) in Zürich. He received a degree in electrical and computer engineering and a Ph.D. on computer graphics and image analysis, both from the University of Saarbrucken, Germany. From 1990 to 1994 Dr. Gross was with the Computer Graphics Center in Darmstadt, where he established and directed the Visual Computing Group. -
Mike Roberts Curriculum Vitae
Mike Roberts +1 (650) 924–7168 [email protected] mikeroberts3000.github.io Research Using photorealistic synthetic data for computer vision; motion planning, trajectory optimization, Interests and control methods for robotics; reconstructing 3D scenes from images; continuous and discrete optimization; submodular optimization; software tools and algorithms for creativity support Education Stanford University Stanford, California Ph.D. Computer Science 2012–2019 Advisor: Pat Hanrahan Dissertation: Trajectory Optimization Methods for Drone Cameras Harvard University Cambridge, Massachusetts Visiting Research Fellow Summer 2013 Advisor: Hanspeter Pfister University of Calgary Calgary, Canada M.S. Computer Science 2010 University of Calgary Calgary, Canada B.S. Computer Science 2007 Employment Intel Labs Seattle, Washington Research Scientist 2021– Mentor: Vladlen Koltun Apple Seattle, Washington Research Scientist 2018–2021 Microsoft Research Redmond, Washington Research Intern Summer 2016, 2017 Mentors: Neel Joshi, Sudipta Sinha Skydio Redwood City, California Research Intern Spring 2016 Mentors: Adam Bry, Frank Dellaert Udacity Mountain View, California Course Developer, Introduction to Parallel Computing 2012–2013 Instructors: John Owens, David Luebke Harvard University Cambridge, Massachusetts Research Fellow 2010–2012 Advisor: Hanspeter Pfister NVIDIA Austin, Texas Developer Tools Programmer Intern Summer 2009 Radical Entertainment Vancouver, Canada Graphics Programmer Intern 2005–2006 Honors And Featured in the Highlights of -
Mike Roberts Curriculum Vitae
Mike Roberts Union Street, Suite , Seattle, Washington, + () / [email protected] http://mikeroberts.github.io Research Using photorealistic synthetic data for computer vision; motion planning, trajectory optimization, Interests and control methods for robotics; reconstructing D scenes from images; continuous and discrete optimization; submodular optimization; software tools and algorithms for creativity support. Education Stanford University Stanford, California Ph.D. Computer Science – Advisor: Pat Hanrahan Dissertation: Trajectory Optimization Methods for Drone Cameras Harvard University Cambridge, Massachusetts Visiting Research Fellow Summer John A. Paulson School of Engineering and Applied Sciences Advisor: Hanspeter Pfister University of Calgary Calgary, Canada M.S. Computer Science University of Calgary Calgary, Canada B.S. Computer Science Employment Apple Seattle, Washington Research Scientist – Microsoft Research Redmond, Washington Research Intern Summer , Advisors: Neel Joshi, Sudipta Sinha Skydio Redwood City, California Research Intern Spring Mentors: Adam Bry, Frank Dellaert Udacity Mountain View, California Course Developer, Introduction to Parallel Computing – Instructors: John Owens, David Luebke Harvard University Cambridge, Massachusetts Research Fellow, John A. Paulson School of Engineering and Applied Sciences – Advisor: Hanspeter Pfister NVIDIA Austin, Texas Developer Tools Programmer Intern Summer Radical Entertainment Vancouver, Canada Graphics Programmer Intern – Honors And Featured in the Highlights -
Monocular Neural Image Based Rendering with Continuous View Control
Monocular Neural Image Based Rendering with Continuous View Control Xu Chen∗ Jie Song∗ Otmar Hilliges AIT Lab, ETH Zurich {xuchen, jsong, otmar.hilliges}@inf.ethz.ch ... ... Figure 1: Interactive novel view synthesis: Given a single source view our approach can generate a continuous sequence of geometrically accurate novel views under fine-grained control. Top: Given a single street-view like input, a user may specify a continuous camera trajectory and our system generates the corresponding views in real-time. Bottom: An unseen hi-res internet image is used to synthesize novel views, while the camera is controlled interactively. Please refer to our project homepagey. Abstract like to view products interactively in 3D rather than from We propose a method to produce a continuous stream discrete view angles. Likewise in map applications it is of novel views under fine-grained (e.g., 1◦ step-size) cam- desirable to explore the vicinity of street-view like images era control at interactive rates. A novel learning pipeline beyond the position at which the photograph was taken. This determines the output pixels directly from the source color. is often not possible because either only 2D imagery exists, Injecting geometric transformations, including perspective or because storing and rendering of full 3D information does projection, 3D rotation and translation into the network not scale. To overcome this limitation we study the problem forces implicit reasoning about the underlying geometry. of interactive view synthesis with 6-DoF view control, taking The latent 3D geometry representation is compact and mean- only a single image as input. We propose a method that ingful under 3D transformation, being able to produce geo- can produce a continuous stream of novel views under fine- grained (e.g., 1◦ step-size) camera control (see Fig.1). -
Thouis R. Jones [email protected]
Thouis R. Jones [email protected] Summary Computational biologist with 20+ years' experience, including software engineering, algorithm development, image analysis, and solving challenging biomedical research problems. Research Experience Connectomics and Dense Reconstruction of Neural Connectivity Harvard University School of Engineering and Applied Sciences (2012-Present) My research is on software for automatic dense reconstruction of neural connectivity. Using using high-resolution serial sectioning and electron microscopy, biologists are able to image nanometer-scale details of neural structure. This data is extremely large (1 petabyte / cubic mm). The tools I develop in collaboration with the members of the Connectomics project allow biologists to analyze and explore this data. Image analysis, high-throughput screening, & large-scale data analysis Curie Institute, Department of Translational Research (2010 - 2012) Broad Institute of MIT and Harvard, Imaging Platform (2007 - 2010) My research focused on image and data analysis for high-content screening (HCS) and large-scale machine learning applied to HCS data. My work emphasized making powerful analysis methods available to the biological community in user-friendly tools. As part of this effort, I cofounded the open-source CellProfiler project with Dr. Anne Carpenter (director of the Imaging Platform at the Broad Institute). PhD Research: High-throughput microscopy and RNAi to reveal gene function. My doctoral research focused on methods for determining gene function using high- throughput microscopy, living cell microarrays, and RNA interference. To accomplish this goal, my collaborators and I designed and released the first open-source high- throughput cell image analysis software, CellProfiler (www.cellprofiler.org). We also developed advanced data mining methods for high-content screens. -
Human-AI Collaboration for Natural Language Generation with Interpretable Neural Networks
Human-AI Collaboration for Natural Language Generation with Interpretable Neural Networks a dissertation presented by Sebastian Gehrmann to The John A. Paulson School of Engineering and Applied Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Computer Science Harvard University Cambridge, Massachusetts May 2020 ©2019 – Sebastian Gehrmann Creative Commons Attribution License 4.0. You are free to share and adapt these materials for any purpose if you give appropriate credit and indicate changes. Thesis advisors: Barbara J. Grosz, Alexander M. Rush Sebastian Gehrmann Human-AI Collaboration for Natural Language Generation with Interpretable Neural Networks Abstract Using computers to generate natural language from information (NLG) requires approaches that plan the content and structure of the text and actualize it in fuent and error-free language. The typical approaches to NLG are data-driven, which means that they aim to solve the problem by learning from annotated data. Deep learning, a class of machine learning models based on neural networks, has become the standard data-driven NLG approach. While deep learning approaches lead to increased performance, they replicate undesired biases from the training data and make inexplicable mistakes. As a result, the outputs of deep learning NLG models cannot be trusted. Wethus need to develop ways in which humans can provide oversight over model outputs and retain their agency over an otherwise automated writing task. This dissertation argues that to retain agency over deep learning NLG models, we need to design them as team members instead of autonomous agents. We can achieve these team member models by considering the interaction design as an integral part of the machine learning model development. -
F I N a L P R O G R a M 1998
1998 FINAL PROGRAM October 18 • October 23, 1998 Sheraton Imperial Hotel Research Triangle Park, NC THE INSTITUTE OF ELECTRICAL IEEE IEEE VISUALIZATION 1998 & ELECTRONICS ENGINEERS, INC. COMPUTER IEEE SOCIETY Sponsored by IEEE Computer Society Technical Committee on Computer Graphics In Cooperation with ACM/SIGGRAPH Sessions include Real-time Volume Rendering, Terrain Visualization, Flow Visualization, Surfaces & Level-of-Detail Techniques, Feature Detection & Visualization, Medical Visualization, Multi-Dimensional Visualization, Flow & Streamlines, Isosurface Extraction, Information Visualization, 3D Modeling & Visualization, Multi-Source Data Analysis Challenges, Interactive Visualization/VR/Animation, Terrain & Large Data Visualization, Isosurface & Volume Rendering, Simplification, Marine Data Visualization, Tensor/Flow, Key Problems & Thorny Issues, Image-based Techniques and Volume Analysis, Engineering & Design, Texturing and Rendering, Art & Visualization Get complete, up-to-date listings of program information from URL: http://www.erc.msstate.edu/vis98 http://davinci.informatik.uni-kl.de/Vis98 Volvis URL: http://www.erc.msstate.edu/volvis98 InfoVis URL: http://www.erc.msstate.edu/infovis98 or contact: Theresa-Marie Rhyne, Lockheed Martin/U.S. EPA Sci Vis Center, 919-541-0207, [email protected] Robert Moorhead, Mississippi State University, 601-325-2850, [email protected] Direct Vehicle Loading Access S Dock Salon Salon Sheraton Imperial VII VI Imperial Convention Center HOTEL & CONVENTION CENTER Convention Center Phones -
Compositional Gan: Learning Conditional Image Composition
Under review as a conference paper at ICLR 2019 COMPOSITIONAL GAN: LEARNING CONDITIONAL IMAGE COMPOSITION Anonymous authors Paper under double-blind review ABSTRACT Generative Adversarial Networks (GANs) can produce images of surprising com- plexity and realism, but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose to model object composition in a GAN framework as a self-consistent composition- decomposition network. Our model is conditioned on the object images from their marginal distributions and can generate a realistic image from their joint distribu- tion. We evaluate our model through qualitative experiments and user evaluations in scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training. Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion. 1I NTRODUCTION Generative Adversarial Networks (GANs) have emerged as a powerful method for generating images conditioned on a given input. The input cue could be in the form of an image (Isola et al., 2017; Zhu et al., 2017; Liu et al., 2017; Azadi et al., 2017; Wang et al., 2017; Pathak et al., 2016), a text phrase (Zhang et al., 2017; Reed et al., 2016b;a; Johnson et al., 2018) or a class label layout (Mirza & Osindero, 2014; Odena et al., 2016; Antoniou et al., 2017). -
Let's Gamble: Uncovering the Impact of Visualization on Risk Perception and Decision-Making
Let’s Gamble: Uncovering the Impact of Visualization on Risk Perception and Decision-Making Melanie Bancilhon Zhengliang Liu Alvitta Ottley [email protected] [email protected] [email protected] Washington Univ. in St. Louis ABSTRACT decision-making. However, it is reasonable to assume that Data visualizations are standard tools for assessing and com- encoding choices can influence the decisions people make. Re- municating risks. However, it is not always clear which de- searchers have shown that visualizations can impact speed [8, signs are optimal or how encoding choices might influence 37], accuracy [37], memorability [3,5], statistical reason- risk perception and decision-making. In this paper, we report ing [27, 30], and judgement [8, 11, 47]. These effects can the findings of a large-scale gambling game that immersed par- have significant repercussions, especially when decisions are ticipants in an environment where their actions impacted their life-altering. Still, a designer can represent the same data bonuses. Participants chose to either enter a draw or receive using different, yet equally theoretically valid visualization guaranteed monetary gains based on five common visualiza- designs [26], and it is sometimes difficult for designers to iden- tion designs. By measuring risk perception and observing tify a poor fit for the data [31]. Therefore, what we need is a decision-making, we showed that icon arrays tended to elicit thorough understanding of which designs lead to (in)accurate economically sound behavior. We also found that people were judgment and how probability distortions influence behavior. more likely to gamble when presented area proportioned tri- At the heart of decision-making with visualization is graphical angle and circle designs. -
Multi-Feature Based Transfer Function Design for 3D Medical Image Visualization
2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI 2010) Multi-feature Based Transfer Function Design for 3D Medical Image Visualization Yi Peng, Li Chen Institute of CG & CAD School of Software, Tsinghua University Beijing, China Abstract—Visualization of three dimensional volumetric medical in multi-dimensional transfer function design. Each of them is data has been widely used. Even though, it still faces a lot of related to a particular local feature. Chen, et al. introduce a challenges, such as helping users find out structure of their volume illustration technique which is shape-aware, as it interest, illustrating features which are significant for diagnosis depends not only on the rendering styles, but also the shape of disease. In our paper, a multi-feature based transfer function styles [6]. Chen’s work improves the expressiveness of volume is provided to improve the quality of visualization. We compute a illustration by focusing more on shape, but it needs the help of multi-feature descriptor for both two-phase clustering and other given illustrations. transfer function design. Moreover, we test the transfer function on several medical datasets to show the efficiency and In addition, [7] presents two visualization techniques for practicability of our method. curve-centric volume reformation to create compelling comparative visualizations, and [8] introduces a texture-based Multi-feature, statistical analysis, pre-segmentation, transfer volume rendering approach which achieves the image quality function, volume rendering, visualization of the best post-shading approaches with far less slices. Their approaches use the latest techniques to accelerate volume I. INTRODUCTION rendering while keep using the general visualization methods. -
Surfacing Visualization Mirages
Surfacing Visualization Mirages Andrew McNutt Gordon Kindlmann Michael Correll University of Chicago University of Chicago Tableau Research Chicago, IL Chicago, IL Seattle, WA [email protected] [email protected] [email protected] ABSTRACT In this paper, we present a conceptual model of these visual- Dirty data and deceptive design practices can undermine, in- ization mirages and show how users’ choices can cause errors vert, or invalidate the purported messages of charts and graphs. in all stages of the visual analytics (VA) process that can lead These failures can arise silently: a conclusion derived from to untrue or unwarranted conclusions from data. Using our a particular visualization may look plausible unless the an- model we observe a gap in automatic techniques for validating alyst looks closer and discovers an issue with the backing visualizations, specifically in the relationship between data data, visual specification, or their own assumptions. We term and chart specification. We address this gap by developing a such silent but significant failures visualization mirages. We theory of metamorphic testing for visualization which synthe- describe a conceptual model of mirages and show how they sizes prior work on metamorphic testing [92] and algebraic can be generated at every stage of the visual analytics process. visualization errors [54]. Through this combination we seek to We adapt a methodology from software testing, metamorphic alert viewers to situations where minor changes to the visual- testing, as a way of automatically surfacing potential mirages ization design or backing data have large (but illusory) effects at the visual encoding stage of analysis through modifications on the resulting visualization, or where potentially important to the underlying data and chart specification.