The Transfer Function Bake-Off
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Ways of Seeing Data: a Survey of Fields of Visualization
• Ways of seeing data: • A survey of fields of visualization • Gordon Kindlmann • [email protected] Part of the talk series “Show and Tell: Visualizing the Life of the Mind” • Nov 19, 2012 http://rcc.uchicago.edu/news/show_and_tell_abstracts.html 2012 Presidential Election REPUBLICAN DEMOCRAT http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money 2012 Presidential Election http://gizmodo.com/5960290/this-is-the-real-political-map-of-america-hint-we-are-not-that-divided 2012 Presidential Election http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money 2012 Presidential Election http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money 2012 Presidential Election http://www.npr.org/blogs/itsallpolitics/2012/11/01/163632378/a-campaign-map-morphed-by-money Clarifying distortions Tube map from 1908 http://www.20thcenturylondon.org.uk/beck-henry-harry http://briankerr.wordpress.com/2009/06/08/connections/ http://en.wikipedia.org/wiki/Harry_Beck Clarifying distortions http://www.20thcenturylondon.org.uk/beck-henry-harry Harry Beck 1933 http://briankerr.wordpress.com/2009/06/08/connections/ http://en.wikipedia.org/wiki/Harry_Beck Clarifying distortions Clarifying distortions Joachim Böttger, Ulrik Brandes, Oliver Deussen, Hendrik Ziezold, “Map Warping for the Annotation of Metro Maps” IEEE Computer Graphics and Applications, 28(5):56-65, 2008 Maps reflect conventions, choices, and priorities “A single map is but one of an indefinitely large -
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. -
Gautam Barua, Director IIT Guwahati • 2011
Speakers of IC3‐2008 to IC3‐2014 Inaugural Talk • 2012: Gautam Barua, Director IIT Guwahati • 2011: Narendra Ahuja, University of Illinois at Urbana‐Champaign, USA (Fellow of ACM, Fellow of the IEEE, Fellow of AAAS) • 2010: Dhanendra Kumar, IAS, Chairman Competition Commission of India • 2009: K.R. Srivathsan, Pro Vice‐Chancellor of IGNOU • 2008: Vineet Nayyar, CEO HCL Technologies Limited Keynote Speakers • 2014 o Martin Henson , Professor, University of Essex, UK o Tirumale K Ramesh, Professor, Amrita University, India o Albert Y. Zomaya, Professor, University of Sydney, Australia o H. J. Siegel, Professor, Colorado State University,USA o Azzedine Boukerche, Professor, University of Ottawa o Krithi Ramamritham, Professor,IIT Bombay o Prem Kalra, Professor,IIT Delhi • 2013 o Zvi Galil, Dean of College of Computing, Georgia Institute of Technology o Dinesh Manocha, Phi Delta Theta/Matthew Mason Distinguished ,Professor of Computer Science at University of North Carolina at Chapel Hill o Yves Robert from LIP, ENS Lyon, France, IEEE Fellow o Laxmi Narayan Bhuyan, Distinguished Professor and Chair, Department of Computer Science and Engineering, University of California, Riverside o R. Govindarajan, IISc Banglore o Srikanth Sundararajan IIT Bhubaneshwar o Rajat Moona, C‐DAC, Pune • 2012 o H. T. Kung, William H. Gates, Harvard University o Nageswara S. V. Rao, Oak Ridge National Laboratory, USA (Fellow of IEEE) o Chandrajit Bajaj, University of Texas at Austin (Fellow of the AAAS, ACM) o Alok Choudhary, John G. Searle , Northwestern University, USA (Fellow of IEEE, ACM and AAAS) o Ishwar Parulkar, Cisco Systems, Bangalore, India o Ramesh Hariharan, Strand Life Sciences, Bangalore, India o Sunil D. -
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. -
Name: Prof. João Manuel Ribeiro Da Silva Tavares Date of Birth: February 12, 1969 Passport Nº: P209475 - PRT
João Manuel R. S. Tavares Curriculum Vitae Name: Prof. João Manuel Ribeiro da Silva Tavares Date of Birth: February 12, 1969 Passport nº: P209475 - PRT Address: Faculdade de Engenharia da Universidade do Porto Departamento de Engenharia Mecânica Rua Dr. Roberto Frias, s/n 4200 - 465 PORTO PORTUGAL Phone: +351 22 508 1487 / +351 22 041 3472 Fax: +351 22 508 1445 Email: [email protected] url: www.fe.up.pt/~tavares Education: Habilitation: Mechanical Engineering, University of Porto – Portugal, 2015 PhD: Electrical and Computer Engineering, University of Porto – Portugal, 2001 (Computational Vision) MSc: Electrical and Computer Engineering, University of Porto – Portugal, 1995 (Industrial informatics) BSc (5 years): Mechanical Engineering, University of Porto – Portugal, 1992 Actual Position: Associate Professor (with Habilitation) at the Faculty of Engineering of the University of Porto, Department of Mechanical Engineering, since December 2011. (www.fe.up.pt) Areas of Research: § Biomedical Engineering; § Biomechanics; § Computational Vision, Image processing and analysis, Medical imaging; § Computer Graphics, Scientific Visualization; 1 João Manuel R. S. Tavares Curriculum Vitae § Modelling and Simulation; § Product development. Research IDs: § Thomson Reuters: www.researcherid.com/rid/M-5305-2013 § Scopus: www.scopus.com/authid/detail.url?authorId=36538110300 § Orcid: http://orcid.org/0000-0001-7603-6526 § ResearchGate: www.researchgate.net/profile/Joao_Tavares2 § Google Scholar: http://scholar.google.pt/citations?user=urdxG3EAAAAJ&hl=en § publons: http://publons.com/a/1170011 Supervisor Projects: Master Thesis Finished: 1. Development of a new nasal irrigation device Ana Rita Gonçalves Salvador Supervisor: João Manuel R. S. Tavares Integrated Master in Mechanical Engineering, University of Porto, 1st semester of 2017/2018, submitted in January 2018, public defense on 16 February 2018 2. -
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.