DOCSLIB.ORG

Visualization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Visualization CSE 694L- Visualization Raghu Machiraju Dreese Laboraories, 779 [email protected] www.cse.ohio-state.edu/~raghu Outline Introduction Visualization pipeline Data acquisition and data structures Basic visual mapping approaches Scalar fields (isosurfaces + volume rendering) Vector and field visualization Perception + Interaction Issues Graphs/Trees + HighD Data 22 Syllabus 33 Sources Selective contributions from - Hanspeter Pfister, Harvard University - Torsten Moeller, Simon Fraser U, Canada - Tamara Munzner, University of British Columbia - Melanie Tory, U of Victoria, Canada - Daniel Weiskopf, TU Stuttgart, Germany 44 1. Introduction What is visualization? Definitions and goals 55 1.1. Definitions and Goals Oxford English Dictionary: to visualize: form a mental vision, image, or picture of (something not visible or present to sight, or of an abstraction); to make visible to the mind or imagination. Picture - Color, texture, patterns, objects - Spatial resolution, stereo, temporal resolution Here: visualization in scientific and technical environments - Not in education, marketing, …. 6 1.1. Definitions and Goals B. McCormick, T. DeFanti, and M. Brown: Visualization is a method of computing. It transforms the symbolic into the geometric, enabling researchers to observe their simulations and computations. Visualization offers a method for seeing the unseen. It enriches the process of scientific discovery and fosters profound and unexpected insights. In many fields it is already revolutionizing the way scientists do science. McCormick, B.H., T.A. DeFanti, M.D. Brown, Visualization in Scientific Computing, Computer Graphics 21(6), November 1987 8 1.1. Definitions and Goals R. Friedhoff and T. Kiley: The standard argument to promote scientific visualization is that today's researchers must consume ever higher volumes of numbers that gush, as if from a fire hose, out of supercomputer simulations or high-powered scientific instruments. If researchers try to read the data, usually presented as vast numeric matrices, they will take in the information at snail's pace. If the information is rendered graphically, however, they can assimilate it at a much faster rate. R.M. Friedhoff and T. Kiely, The Eye of the Beholder, Computer Graphics World 13(8), pp. 46-, August 1990 9 1.1. Definitions and Goals R.B. Haber and D. A. McNabb: The use of computer imaging technology as a tool for comprehending data obtained by simulation or physical measurement by integration of older technologies, including computer graphics, image processing, computer vision, computer- aided design, geometric modeling, approximation theory, perceptual psychology, and user interface studies. R.B. Haber and D. A. McNabb, Visualization Idioms: A Conceptual Model for Scientific Visualization Systems, in Visualization in Scientific Computing, IEEE Computer Society Press 1990. 10 1.1. Definitions and Goals R.A. Earnshaw: Scientific Visualization is concerned with exploring data and information in such a way as to gain understanding and insight into the data. The goal of scientific visualization is to promote a deeper level of understanding of the data under investigation and to foster new insight into the underlying processes, relying on the humans' powerful ability to visualize. In a number of instances, the tools and techniques of visualization have been used to analyze and display large volumes of, often time-varying, multidimensional data in such a way as to allow the user to extract significant features and results quickly and easily. K.W. Brodlie, L.A. Carpenter, R.A. Earnshaw, J.R. Gallop, R.J. Hubbard, A.M. Mumford, C.D. Osland, P. Quarendon, Scientific Visualization, Techniques and Applications, Springer-Verlag, 1992. 1010 1.1. Definitions and Goals J. Foley and B. Ribarsky: A useful definition of visualization might be the binding (or mapping) of data to representations that can be perceived. The types of bindings could be visual, auditory, tactile, etc., or a combination of these. J. Foley and B. Ribarsky, Next-generation Data Visualization Tools, in Scientific Visualization, 1994, Advances and Challenges, Academic Press. 1111 1.1. Definitions and Goals H. Senay and E. Ignatius Scientific data visualization supports scientists and relations, prove or disprove hypotheses, and discover new phenomena using graphical techniques. The primary objective in data visualization is to gain insight into an information space by mapping data onto graphical primitives. H. Senay and E. Ignatius, A Knowledge-Based System for Visualization Design, IEEE Computer Graphics and Applications, pp. 36-47, November 1994. 1212 1.1. Definitions and Goals Insight and analysis - Extract the information content - Make things/coherences visible that are not apparent - Analyze the data by means of the visual representation Communication - Allow the non–expert to understand • Present specific information in a way that all of us understand - Guide the expert into the right direction 1313 • Steering (Exploration) - Interactively control and drive your application 1.1. Definitions and Goals Educational goals Visualization specialist Theory - Classification - Algorithms - Visual design Application - Methods - Visualization packages • Experience - How to visualize something in the best way 1414 1.2. History Techniques for finding visual representations for abstract data are not new! 1515 1.2. History Map of Catal Hyük 6200 BC Excavation Reconstruction 1616 1.2. History Map on clay of Ga-Sur 2500 BC • Map on clay • Reconstruction 1717 1.2. History Sung Dynasty, China (1137 BC) 1818 1.2. History Ptolemaic map of the world by Johan Scotus (1505 AD) 20 1.2. History Time series: inclination of planets (10th century) 2020 1.2. History Map of tracks of Yü the Great (11th century) 2121 1.2. History Vector visualization (1686) Height field (1879) Streamlines, arrow plots by Halley Census data by Perozzo 1.2. History Napoleon‘s campaign against Russia (1812/13) Cartography (1861) 1.2. History 1987 NSF Advisory Panel on Graphics and Image Processing - Computer experiments allow access to new worlds - Real experiments are too expensive, too dangerous, etc. - Arbitrary large and small time scales and spatial dimensions Direct implications - The data flood from supercomputer simulations can only be dealt with visually - This task needs a visualization specialist and an interdisciplinary team - New developments in hardware, software, nets, standards are necessary Advantages in the long term will be - Faster insight - Faster product–development cycles - Stronger position in global competition Suggestion: spend lots of money to support scientific visualization 1.3. Examples 2525 1.3. Examples Deutsches Klimarechenzentrum DKRZ Simulation of climate phenomena (volcanoes, atmosphere) 1.3. Examples Deutsches Klimarechenzentrum DKRZ Simulation of global warming. Worst case (left), with drastic political measures (right) 2727 1.3. Examples AVS Express Visualization of traffic in Dallas, Texas 1.3. Examples AVS Express Medical imaging: therapy planning 2929 1.3. Examples AVS Express Medical imaging: therapy evaluation 3030 1.3. Examples Varying the transfer function – the visible woman 1.3. Examples Aneurysms: vessel deformations in CTA 1.3. Examples 7T MRI Image 3D/4D Fetal Imaging CT Small Animal Imaging Coronary Artery Ulcerated Plaque Heart - CT Visualization - MRI 1.3. Examples CT scan 3434 1.3. Examples Blue: nuclei indicator Purple: neuron indicator Green: blood vessel wall indicator Red: astrocyte indicator (GFAP) Yellow: microglia indicator (Iba-1) 50 µm Prof. Badri Roysam, RPI 1.3. Examples Prof. Sean Megason, Harvard Medical School 800 Timesteps, Movie 1.3. Examples Jens Rittscher, GE Global Researcher More ZebraFish Body Parts - Eye 1.3. ExamplesDrs. Knopp, Sammett, Radiology, OSU Drs. Peter Basser, Carlo Pierpaoli, NIH DWI Tensors Fibers 1.3. Examples Dr. Morocz, Radiology, Harvard Medical 1.3. Examples AChE-FAS complex, showing that the snake venom blocks the entrance to the gorge. The active residues are shown as ball and stick. 1.3. Examples The secondary structure (alpha helices and beta sheets) of the protein Acetylcholinesterase. The aromatic residues (bonds) and the neurotransmitter Acetylcholine (molecular surface) lodged in the active site are also shown. [J. Sussman] 1.3. Examples Relation of secondary structure (alpha-helix) to primary structure formed by the polypeptide chain of amino acids. The helix is stabilized by Hydrogen bonding. 1.3. Examples Aromatic residues in active site (ball and stick) of AChE enzyme with molecular surface of ACh molecule. 1.3. Examples Active site in AChE showing Tachrine molecule used in the experimental drug Cognix to treat Alzheimer's disease. By inhibiting the enzyme's activity, Tachrine prevents the breakdown of the neurotransmitter ACh. 4444 1.3. Examples Visualization and Animation Laboratory at Center of Scientific Computing Finland Simulation of the docking of the CBH1 tail on a cellulose surface. 1.3. Examples Simulated events in the Relativistic Heavy Ion Collider Experiment PHENIX. Hits at the detector appear as yellow glyphs. Blue lines trace the probable tracks of products from the collision. [J. Mitchell] 1.3. Examples Generation of a ring of dust around a star 4747 1.3. Examples Simulation of a flow around a wheel (3D Line Integral Convolution) 1.3. Examples Flow visualization 1.3. Examples Flow visualization 5050 1.3. Examples Computed flow through a cerebral aneurysm. 5151 1.3. Examples Pseudo color image of the spray pattern generated
Load more

Recommended publications
  • Bio 219 Biomedical Imaging and Scientific Visualization
    Bio 219 Biomedical Imaging and Scientific Visualization Ch. Zollikofer M. Ponce de León Organization • OLAT: – course scripts (pdf) • website: www.aim.uzh.ch/morpho/wiki/Teaching/SciVis – course scripts (pdf); passwd: scivisdocs – link collection (tutorials, applets, software/data downloads, ...) • book (background information): Zollikofer & Ponce de León, Virtual Reconstruction. A Primer in Computer-assisted Paleontology and Biomedicine (NY: Wiley, 2005) CHF 55 • final exam – Monday, 26. May 2014, 1015-1100 BioMedImg & SciVis • at the intersection between – theory/practice of image data acquisition – computer graphics – medical diagnostics – computer-assisted paleoanthropology Grotte Chauvet, France Biomedical Imaging • acquisition • processing • analysis • visualization ... of biomedical data Scientific Visualization visual... • representation (cf. data presentation) • exploration • analysis ...of scientific data aims of this course • provide theoretical (and practical) foundations of – image data acquisition, storage, retrieval – image data processing and analysis – image data visualization/rendering • establish links between – real-life vision and computer vision – computer science and biomedical sciences – theory and practice of handling biomedical data contents • real-life vision • computers and data representation • 2D image data acquisition • 3D image data acquisition • biomedical image processing in 2D and 3D • biomedical image data visualization and interaction biomedical data types of data data flow humans and computers facts and data • facts exist by definition (±independent of the observer): – females and males – humans and Neanderthals – dogs and wolves • data are generated through observation: – number of living human species: 1 – proportion of females to males at birth: 49/51 – nr. of wolves per square km biomedical data: general • physical/physiological data about the human body: – density – temperature – pressure – mass – chemical composition biomedical data: space and time • spatial – 1D – 2D – 3D • temporal • spatiotemporal (4D) ..
    [Show full text]
  • ARTG-2306-CRN-11966-Graphic Design I Computer Graphics
    Course Information Graphic Design 1: Computer Graphics ARTG 2306, CRN 11966, Section 001 FALL 2019 Class Hours: 1:30 pm - 4:20 pm, MW, Room LART 411 Instructor Contact Information Instructor: Nabil Gonzalez E-mail: [email protected] Office: Office Hours: Instructor Information Nabil Gonzalez is your instructor for this course. She holds an Associate of Arts degree from El Paso Community College, a double BFA degree in Graphic Design and Printmaking from the University of Texas at El Paso and an MFA degree in Printmaking from the Rhode Island School of Design. As a studio artist, Nabil’s work has been focused on social and political views affecting the borderland as well as the exploration of identity, repetition and erasure. Her work has been shown throughout the United State, Mexico, Colombia and China. Her artist books and prints are included in museum collections in the United States. Course Description Graphic Design 1: Computer Graphics is an introduction to graphics, illustration, and page layout software on Macintosh computers. Students scan, generate, import, process, and combine images and text in black and white and in color. Industry standard desktop publishing software and imaging programs are used. The essential applications taught in this course are: Adobe Illustrator, Adobe Photoshop and Adobe InDesign. Course Prerequisite Information Course prerequisites include ARTF 1301, ARTF 1302, and ARTF 1304 each with a grade of “C” or better. Students are required to have a foundational understanding of the elements of design, the principles of composition, style, and content. Additionally, students must have developed fundamental drawing skills. These skills and knowledge sets are provided through the Department of Art’s Foundational Courses.
    [Show full text]
  • Scientific Visualization
    GeoVisualization “Geovisualization integrates approaches from visualization in scientific computing (ViSC), cartography, image analysis, information systems (GISystems) to provide theory, methods, and tools for visual exploration, analysis, synthesis, and presentation of geospatial data” -- International cartographic association commission (2001) Point/line/surface, 3D, spatial-temporal 1 Point: gas stations on Google map Location information only. 2 Point: geo-temporal data 3 Point: vectors 4 Point: bricks & colors 5 Line: Google map 6 Line: Facebook (dense edges) 7 Line: bundling technique Population migration: Airline routes: 8 Region: contours (boundaries only) 9 Region: color (filled regions) 10 Health Data (Disease Distributions) 11 Cartogram: scale and deform regions to reflect the size of the attributes 12 Multi-relationship: line/bubble set Multiple relationships Avoid re-layout 13 3D Map (2.5 Dimension) 14 3D Map: issues Realism vs. Abstraction Distraction Occlusion Applications: – Travel guide – City planning/simulation 15 3D Map: occlusion & landmark 16 Spatial-temporal data Time stamps/labels on 2D map Space-time cube Color curves Attribute changes 17 Time-trajectory (Tornado path) 18 Space-Time Cube 19 Trajectory Wall (C. Tominsk, et al) 20 Color Curves Using curves in color space to represent time. 21 Using Color Curves to Draw Taxis Trajectories on 2D Maps 22 Attribute changes Robbery geo-temporal data 23 Attribute changes (Health Data) 24 Interactive Techniques in InfoVis Select: mark something as interesting
    [Show full text]
  • Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering
    Eurographics Conference on Visualization (EuroVis) 2017 Volume 36 (2017), Number 3 J. Heer, T. Ropinski and J. van Wijk (Guest Editors) Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering Donghao Ren1, Bongshin Lee2, and Tobias Höllerer1 1University of California, Santa Barbara, United States 2Microsoft Research, Redmond, United States Abstract Web-based visualization libraries are in wide use, but performance bottlenecks occur when rendering, and especially animating, a large number of graphical marks. While GPU-based rendering can drastically improve performance, that paradigm has a steep learning curve, usually requiring expertise in the computer graphics pipeline and shader programming. In addition, the recent growth of virtual and augmented reality poses a challenge for supporting multiple display environments beyond regular canvases, such as a Head Mounted Display (HMD) and Cave Automatic Virtual Environment (CAVE). In this paper, we introduce a new web-based visualization library called Stardust, which provides a familiar API while leveraging GPU’s processing power. Stardust also enables developers to create both 2D and 3D visualizations for diverse display environments using a uniform API. To demonstrate Stardust’s expressiveness and portability, we present five example visualizations and a coding playground for four display environments. We also evaluate its performance by comparing it against the standard HTML5 Canvas, D3, and Vega. Categories and Subject Descriptors (according to ACM CCS):
    [Show full text]
  • Civic Participation and Empowerment Through Visualization
    SIGRAD 2015 L. Kjelldahl and C. Peters (Editors) Civic Participation and Empowerment through Visualization Samuel Bohmany Department of Computer and Systems Sciences, Stockholm University, Sweden Abstract This article elaborates on the use of data visualization to promote civic participation and democratic engage- ment. The power and potential of data visualization is examined through a brief historical overview and four interconnected themes that provide new opportunities for electronic participation research: data storytelling, in- fographics, data physicalization, and quantified self. The goal is to call attention to this space and encourage a larger community of researchers to explore the possibilities that data visualization can bring. Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Interfaces and Presentation]: Screen design—User-centered design 1. Introduction The authors acknowledge that the deliberative approach to civic dialogue is “unduly restrictive, discounting other im- Since the advent of the web in the early 1990s, prospects portant ways of making, receiving, and contesting public of electronic democracy have been viewed as heralding in claims”. They therefore encourage researchers in the field to a new era of political participation and civic engagement. be more open to a wider range of practices and technologies However, empirical studies suggest most initiatives to date and suggest some of the most innovative research is being have failed to live up to expectations despite large invest- done in the area of computer-supported argument mapping ments in research. For instance, Chadwick [Cha08] states and visualization. However, the visualization research they that “the reality of online deliberation, whether judged in are referring to have primarily focused on facilitating large- terms of quantity, its quality, or its impact on political be- scale online deliberation within a conventional rationalistic haviour and policy outcomes, is far removed from the ide- framework.
    [Show full text]
  • 4.3 Discovering Fractal Geometry in CAAD
    4.3 Discovering Fractal Geometry in CAAD Francisco Garcia, Angel Fernandez*, Javier Barrallo* Facultad de Informatica. Universidad de Deusto Bilbao. SPAIN E.T.S. de Arquitectura. Universidad del Pais Vasco. San Sebastian. SPAIN * Fractal geometry provides a powerful tool to explore the world of non-integer dimensions. Very short programs, easily comprehensible, can generate an extensive range of shapes and colors that can help us to understand the world we are living. This shapes are specially interesting in the simulation of plants, mountains, clouds and any kind of landscape, from deserts to rain-forests. The environment design, aleatory or conditioned, is one of the most important contributions of fractal geometry to CAAD. On a small scale, the design of fractal textures makes possible the simulation, in a very concise way, of wood, vegetation, water, minerals and a long list of materials very useful in photorealistic modeling. Introduction Fractal Geometry constitutes today one of the most fertile areas of investigation nowadays. Practically all the branches of scientific knowledge, like biology, mathematics, geology, chemistry, engineering, medicine, etc. have applied fractals to simulate and explain behaviors difficult to understand through traditional methods. Also in the world of computer aided design, fractal sets have shown up with strength, with numerous software applications using design tools based on fractal techniques. These techniques basically allow the effective and realistic reproduction of any kind of forms and textures that appear in nature: trees and plants, rocks and minerals, clouds, water, etc. For modern computer graphics, the access to these techniques, combined with ray tracing allow to create incredible landscapes and effects.
    [Show full text]
  • Daftar Program Untuk Komputer Grafik Dan Pengolahan Citra
    Daftar program untuk Komputer Grafik dan Pengolahan Citra I Made Wiryana 1 Nopember 2012 Daftar Isi 1 OpenCV 1 2 Scilab Image Processing Toolbox 2 3 VIPS 2 4 ImageJ 2 5 Marvin 2 6 MeVisLab 3 7 MVTH 3 8 Gephex 3 9 Fiji 3 10 MathMap 4 11 VTK 4 12 Tulip 4 13 PreFuse 4 1 OpenCV OpenCV - Open Source Computer Vision Library [http://www.opencv.org] is an open-source BSD- licensed library that includes several hundreds of computer vision algorithms. The document de- scribes the so-called OpenCV 2.x API, which is essentially a C++ API, as opposite to the C-based OpenCV 1.x API. The latter is described in opencv1x.pdf. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algori- thms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire 1 scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reali- ty, etc.
    [Show full text]
  • Volume Rendering
    Volume Rendering 1.1. Introduction Rapid advances in hardware have been transforming revolutionary approaches in computer graphics into reality. One typical example is the raster graphics that took place in the seventies, when hardware innovations enabled the transition from vector graphics to raster graphics. Another example which has a similar potential is currently shaping up in the field of volume graphics. This trend is rooted in the extensive research and development effort in scientific visualization in general and in volume visualization in particular. Visualization is the usage of computer-supported, interactive, visual representations of data to amplify cognition. Scientific visualization is the visualization of physically based data. Volume visualization is a method of extracting meaningful information from volumetric datasets through the use of interactive graphics and imaging, and is concerned with the representation, manipulation, and rendering of volumetric datasets. Its objective is to provide mechanisms for peering inside volumetric datasets and to enhance the visual understanding. Traditional 3D graphics is based on surface representation. Most common form is polygon-based surfaces for which affordable special-purpose rendering hardware have been developed in the recent years. Volume graphics has the potential to greatly advance the field of 3D graphics by offering a comprehensive alternative to conventional surface representation methods. The object of this thesis is to examine the existing methods for volume visualization and to find a way of efficiently rendering scientific data with commercially available hardware, like PC’s, without requiring dedicated systems. 1.2. Volume Rendering Our display screens are composed of a two-dimensional array of pixels each representing a unit area.
    [Show full text]
  • Course Title: Principles of Scientific Visualization Course Number: 9400110 Course Credit: 1 Course Description: This Course
    Course Title: Principles of Scientific Visualization Course Number: 9400110 Course Credit: 1 Course Description: This course provides students with instruction in the evolution and underlying principles of scientific visualization, including two-dimensional representation of scientific and other forms of data. Included in the content is the use of color and other graphical elements such as vector and bitmap images in different presentation techniques. Students will also learn about the use of charts and graphs in representing data and the software tools used to produce them. The ultimate output of this course is a design portfolio created by the student from a scenario. The portfolio should include a narrative description of the scenario, the approach to data collection, resulting charts and graphs, and an interpretation of each chart/graph. Research references should be cited appropriately. Consideration should be given to having students produce the portfolio using presentation software. CTE Standards and Benchmarks NGSSS-Sci 28.0 Describe scientific & technical visualization. – The student will be able to: 28.01 Define scientific and technical visualization and provide examples of each. 28.02 Explain the importance of scientific visualization and its applicability to various industries. 28.03 Provide examples of 2-D and 3D rendered visualizations. 29.0 Describe the historical significance of scientific & technical visualization. – The student will be able to: 29.01 Describe the evolution of drawings from cave through perspective drawings to photography, television, and the Internet. 29.02 Define and describe the elements contained on various types of maps (e.g., road, topographic, aeronautical, weather, concept, and gene). SC.912.L.14.4; SC.912.E.5.8; 30.0 Describe the technological advancements of scientific & technical visualization.
    [Show full text]
  • Cryptic Inoviruses Revealed As Pervasive in Bacteria and Archaea Across Earth’S Biomes
    ARTICLES https://doi.org/10.1038/s41564-019-0510-x Corrected: Author Correction Cryptic inoviruses revealed as pervasive in bacteria and archaea across Earth’s biomes Simon Roux 1*, Mart Krupovic 2, Rebecca A. Daly3, Adair L. Borges4, Stephen Nayfach1, Frederik Schulz 1, Allison Sharrar5, Paula B. Matheus Carnevali 5, Jan-Fang Cheng1, Natalia N. Ivanova 1, Joseph Bondy-Denomy4,6, Kelly C. Wrighton3, Tanja Woyke 1, Axel Visel 1, Nikos C. Kyrpides1 and Emiley A. Eloe-Fadrosh 1* Bacteriophages from the Inoviridae family (inoviruses) are characterized by their unique morphology, genome content and infection cycle. One of the most striking features of inoviruses is their ability to establish a chronic infection whereby the viral genome resides within the cell in either an exclusively episomal state or integrated into the host chromosome and virions are continuously released without killing the host. To date, a relatively small number of inovirus isolates have been extensively studied, either for biotechnological applications, such as phage display, or because of their effect on the toxicity of known bacterial pathogens including Vibrio cholerae and Neisseria meningitidis. Here, we show that the current 56 members of the Inoviridae family represent a minute fraction of a highly diverse group of inoviruses. Using a machine learning approach lever- aging a combination of marker gene and genome features, we identified 10,295 inovirus-like sequences from microbial genomes and metagenomes. Collectively, our results call for reclassification of the current Inoviridae family into a viral order including six distinct proposed families associated with nearly all bacterial phyla across virtually every ecosystem.
    [Show full text]
  • Animated Presentation of Static Infographics with Infomotion
    Eurographics Conference on Visualization (EuroVis) 2021 Volume 40 (2021), Number 3 R. Borgo, G. E. Marai, and T. von Landesberger (Guest Editors) Animated Presentation of Static Infographics with InfoMotion Yun Wang1, Yi Gao1;2, Ray Huang1, Weiwei Cui1, Haidong Zhang1, and Dongmei Zhang1 1Microsoft Research Asia 2Nanjing University (a) 5% (b) time Element Animation effect Meats, sweets slice spin link wipe dot zoom 35% icon zoom 10% Whole grains, title zoom OliVer oil pasta, beans, description wipe whole grain bread Mediterranean Diet 20% 30% 30% Vegetables and fruits Fish, seafood, poultry, Vegetables and fruits dairy food, eggs Figure 1: (a) An example infographic design. (b) The animated presentations for this infographic show the start time, duration, and animation effects applied to the visual elements. InfoMotion automatically generates animated presentations of static infographics. Abstract By displaying visual elements logically in temporal order, animated infographics can help readers better understand layers of information expressed in an infographic. While many techniques and tools target the quick generation of static infographics, few support animation designs. We propose InfoMotion that automatically generates animated presentations of static infographics. We first conduct a survey to explore the design space of animated infographics. Based on this survey, InfoMotion extracts graphical properties of an infographic to analyze the underlying information structures; then, animation effects are applied to the visual elements in the infographic in temporal order to present the infographic. The generated animations can be used in data videos or presentations. We demonstrate the utility of InfoMotion with two example applications, including mixed-initiative animation authoring and animation recommendation.
    [Show full text]
  • Information Graphics Design Challenges and Workflow Management Marco Giardina, University of Neuchâtel, Switzerland, Pablo Medi
    Online Journal of Communication and Media Technologies Volume: 3 – Issue: 1 – January - 2013 Information Graphics Design Challenges and Workflow Management Marco Giardina, University of Neuchâtel, Switzerland, Pablo Medina, Sensiel Research, Switzerland Abstract Infographics, though still in its infancy in the digital world, may offer an opportunity for media companies to enhance their business processes and value creation activities. This paper describes research about the influence of infographics production and dissemination on media companies’ workflow management. Drawing on infographics examples from New York Times print and online version, this contribution empirically explores the evolution from static to interactive multimedia infographics, the possibilities and design challenges of this journalistic emerging field and its impact on media companies’ activities in relation to technology changes and media-use patterns. Findings highlight some explorative ideas about the required workflow and journalism activities for a successful inception of infographics into online news dissemination practices of media companies. Conclusions suggest that delivering infographics represents a yet not fully tapped opportunity for media companies, but its successful inception on news production routines requires skilled professionals in audiovisual journalism and revised business models. Keywords: newspapers, visual communication, infographics, digital media technology © Online Journal of Communication and Media Technologies 108 Online Journal of Communication and Media Technologies Volume: 3 – Issue: 1 – January - 2013 During this time of unprecedented change in journalism, media practitioners and scholars find themselves mired in a new debate on the storytelling potential of data visualization narratives. News organization including the New York Times, Washington Post and The Guardian are at the fore of innovation and experimentation and regularly incorporate dynamic graphics into their journalism products (Segel, 2011).
    [Show full text]