From Surface Rendering to Volume
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Computer Graphics and Visualization
European Research Consortium for Informatics and Mathematics Number 44 January 2001 www.ercim.org Special Theme: Computer Graphics and Visualization Next Issue: April 2001 Next Special Theme: Metacomputing and Grid Technologies CONTENTS KEYNOTE 36 Physical Deforming Agents for Virtual Neurosurgery by Michele Marini, Ovidio Salvetti, Sergio Di Bona 3 by Elly Plooij-van Gorsel and Ludovico Lutzemberger 37 Visualization of Complex Dynamical Systems JOINT ERCIM ACTIONS in Theoretical Physics 4 Philippe Baptiste Winner of the 2000 Cor Baayen Award by Anatoly Fomenko, Stanislav Klimenko and Igor Nikitin 38 Simulation and Visualization of Processes 5 Strategic Workshops – Shaping future EU-NSF collaborations in in Moving Granular Bed Gas Cleanup Filter Information Technologies by Pavel Slavík, František Hrdliãka and Ondfiej Kubelka THE EUROPEAN SCENE 39 Watching Chromosomes during Cell Division by Robert van Liere 5 INRIA is growing at an Unprecedented Pace and is starting a Recruiting Drive on a European Scale 41 The blue-c Project by Markus Gross and Oliver Staadt SPECIAL THEME 42 Augmenting the Common Working Environment by Virtual Objects by Wolfgang Broll 6 Graphics and Visualization: Breaking new Frontiers by Carol O’Sullivan and Roberto Scopigno 43 Levels of Detail in Physically-based Real-time Animation by John Dingliana and Carol O’Sullivan 8 3D Scanning for Computer Graphics by Holly Rushmeier 44 Static Solution for Real Time Deformable Objects With Fluid Inside by Ivan F. Costa and Remis Balaniuk 9 Subdivision Surfaces in Geometric -
Technology-Report Visual Computing
Visual Computing Technology Report Vienna, January 2017 Introduction Dear Readers, Vienna is among the top 5 ICT metropolises in Europe. Around 5,800 ICT enterprises generate sales here of around 20 billion euros annually. The approximately 8,900 national and international ICT companies in the "Vienna Region" (Vienna, Lower Austria and Burgenland) are responsible for roughly two thirds of the total turnover of the ICT sector in Austria. According to various studies, Vienna scores especially strongly in innovative power, comprehensive support for start- ups, and a strong focus on sustainability. Vienna also occupies the top positions in multiple "Smart City" rankings. This location is also appealing due to its research- and technology-friendly climate, its geographical and cultural vicinity to the growth markets in the East, the high quality of its infrastructure and education system, and last but not least the best quality of life worldwide. In order to make optimal use of this location's potential, the Vienna Business Agency functions as an information and cooperation platform for Viennese technology developers. It networks enterprises with development partners and leading economic, scientific and municipal administrative customers, and supports the Viennese enterprises with targeted monetary funding and a variety of consulting and service offerings. Support in this area is also provided by the technology platform of the Vienna Business Agency. At technologieplattform.wirtschaftsagentur.at, Vienna businesses and institutions from the field of technology can present their innovative products, services and prototypes as well as their research expertise, and find development partners and pilot customers. The following technology report offers an overview of the many trends and developments in the field of Entertainment Computing. -
Catalogue Description
INF 454: Data Visualization and User Interface Design Spring 2016 Syllabus Day/Times: TBD (4 Units) Location: TBD Instructor: Dr. Luciano Nocera Email: [email protected] Phone: (213) 740-9819 Office: PHE 412 Course TA: TBD Email: TBD Office Hours: TBD IT Support: TBD Email: TBD Office Hours: TBD Instructor’s Office Hours: TBD; other hours by appointment only. Students are advised to make appointments ahead of time in any event and be specific with the subject matter to be discussed. Students should also be prepared for their appointment by bringing all applicable materials and information. Catalogue Description One of the cornerstones of analytics is presenting the data to customers in a usable fashion. When considering the design of systems that will perform data analytic functions, both the interface for the user and the graphical depictions of data are of utmost importance, as it allows for more efficient and effective processing, leading to faster and more accurate results. To foster the best tools possible, it is important for designers to understand the principles of user interfaces and data visualization as the tools they build are used by many people - with technical and non-technical background - to perform their work. In this course, students will apply the fundamentals and techniques in a semester-long group project where they design, build and test a responsive application that runs on mobile devices and desktops and that includes graphical depictions of data for communication, analysis, and decision support. Short description: Foundational course focusing on the design, creation, understanding, application, and evaluation of data visualization and user interface design for communicating, interacting and exploring data. -
Math 253: Mathematical Methods for Data Visualization – Course Introduction and Overview (Spring 2020)
Math 253: Mathematical Methods for Data Visualization – Course introduction and overview (Spring 2020) Dr. Guangliang Chen Department of Math & Statistics San José State University Math 253 course introduction and overview What is this course about? Context: Modern data sets often have hundreds, thousands, or even millions of features (or attributes). ←− large dimension Dr. Guangliang Chen | Mathematics & Statistics, San José State University2/30 Math 253 course introduction and overview This course focuses on the statistical/machine learning task of dimension reduction, also called dimensionality reduction, which is the process of reducing the number of input variables of a data set under consideration, for the following benefits: • It reduces the running time and storage space. • Removal of multi-collinearity improves the interpretation of the parameters of the machine learning model. • It can also clean up the data by reducing the noise. • It becomes easier to visualize the data when reduced to very low dimensions such as 2D or 3D. Dr. Guangliang Chen | Mathematics & Statistics, San José State University3/30 Math 253 course introduction and overview There are two different kinds of dimension reduction approaches: • Feature selection approaches try to find a subset of the original features variables. Examples: subset selection, stepwise selection, Ridge and Lasso regression. ←− Already covered in Math 261A • Feature extraction transforms the data in the high-dimensional space to a space of fewer dimensions. ←− Focus of this course Examples: principal component analysis (PCA), ISOmap, and linear discriminant analysis (LDA). Dr. Guangliang Chen | Mathematics & Statistics, San José State University4/30 Math 253 course introduction and overview Dimension reduction methods to be covered in this course: • Linear projection methods: – PCA (for unlabled data), – LDA (for labled data) • Nonlinear embedding methods: – Multidimensional scaling (MDS), ISOmap – Locally linear embedding (LLE) – Laplacian eigenmaps Dr. -
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. -
Revolutionizing the Way We Capture, Image and Display the Visual World
Center for Visual Computing Revolutionizing the way we capture, image and display the visual world. At the UC San Diego Center for Visual Computing, we are researching and developing a future in which we can render photograph-quality images instantly on mobile devices. A future in which computers and wearable devices have the ability to see and understand the physical world just as humans do. A future in which real and virtual content merge seamlessly across different platforms. The opportunities in communication, health and medicine, city planning, entertainment, 3-D printing and more are vast… and emerging quickly. To pursue these kinds of research projects at the Center for Visual Computing, we draw together computer graphics, augmented and virtual reality, computational imaging and computer vision. Unique Capabilities and Facilities Our immersive virtual and augmented-reality test beds in UC San Diego’s Qualcomm Institute are an ideal laboratory for our software-intensive work which extends from the theoretical and computational to 3-D immersion. Join us in building this future. Unbuilt Courtyard House by Ludwig Mies van der Rohe. This rendering demon- strates how photon mapping can simulate all types of light scattering. MOBILE VISUAL COMPUTING INTERACTIVE DIGITAL UNDERSTANDING PEOPLE AND AND DIGITAL IMAGING (AUGMENTED) REALITY THEIR SURROUNDINGS • New techniques to capture the visual • Achieving photograph-quality images • Computer vision systems with human- environment via mobile devices at interactive frame rates to enable level -
Introduction to Geospatial Data Visualization
Introduction to Geospatial Data Visualization Lecturers: Viktor Lagutov, Katalin Szende, Joszef Laszlovsky. Ruben Mnatsakanian Duration: Fall term (September – December) Credits: 2 Course level: PhD / MA Maximum number of students: 15 Pre-requisites: none Software: GoogleEarthPro, qGIS, online mapping tools (e.g. GoogleMaps, ArcGISonline) Rapidly growing cross-disciplinary recognition and availability made Geospatial Methods in general, and Mapping, in particular, a popular approach in many research areas. Till recently, maps development had been a prerogative of cartographers and, later, experts in specialized mapping packages. Latest advances in hardware and software have opened this area to researchers in other disciplines and allowed them to enhance traditional research methods. The wide spectrum of such technologies and approaches is often referred as Geographic Information Systems (GIS) and includes, among others, mapping packages, geospatial analysis, crowdsourcing with mobile technologies, drones, online interactive data publishing. The geospatial literacy is becoming not an optional advantage for researchers and policy officers, but a basic requirement for many employers. The aim of the course is to develop basic understanding of spatially referenced data use and to explore potential applications of GIS in various research areas. The sessions provide both theoretical understanding and practical use of geospatial data and technologies for mapping societal and environmental phenomena. Students will learn basic features of GIS packages and the ways to utilize them for own research. The course is focused on practical skills in geospatial data visualization (mapping) and consists of • Theoretical sessions on principles of geospatial data visualization, cartography and GIS basics; • Practicals on learning GIS methods and getting mapping skills using free open source packages; • Supervised and independent students’ work on individual course projects. -
Tree-Map: a Visualization Tool for Large Data
TREE-MAP: A VISUALIZATION TOOL FOR LARGE DATA Mahipal Jadeja Kesha Shah DA-IICT DA-IICT Gandhinagar,Gujarat Gandhinagar,Gujarat India India Tel:+91-9173535506 Tel:+91-7405217629 [email protected] [email protected] ABSTRACT 1. INTRODUCTION Traditional approach to represent hierarchical data is to use Tree-Maps are used to present hierarchical information on directed tree. But it is impractical to display large (in terms 2-D[1] (or 3-D [2]) displays. Tree-maps offer many features: of size as well complexity) trees in limited amount of space. based upon attribute values users can specify various cate- In order to render large trees consisting of millions of nodes gories, users can visualize as well as manipulate categorized efficiently, the Tree-Map algorithm was developed. Even file information and saving of more than one hierarchy is also system of UNIX can be represented using Tree-Map. Defi- supported [3]. nition of Tree-Maps is recursive: allocate one box for par- Various tiling algorithms are known for tree-maps namely: ent node and children of node are drawn as boxes within Binary tree, mixed treemaps, ordered, slice and dice, squar- it. Practically, it is possible to render any tree within prede- ified and strip. Transition from traditional representation fined space using this technique. It has applications in many methods to Tree-Maps are shown below. In figure 1 given fields including bio-informatics, visualization of stock port- hierarchical data and equivalent tree representation of given folio etc. This paper supports Tree-Map method for data data are shown. One can consider nodes as sets, children integration aspect of knowledge graph. -
Connected 2D and 3D Visualizations for the Interactive Exploration of Spatial Information
CONNECTED 2D AND 3D VISUALIZATIONS FOR THE INTERACTIVE EXPLORATION OF SPATIAL INFORMATION S. Bleisch *, S. Nebiker FHNW, University of Applied Sciences Northwestern Switzerland, Institute of Geomatics Engineering, CH-4132 Muttenz, Switzerland - (susanne.bleisch, stephan.nebiker)@fhnw.ch KEY WORDS: Geovisualization, Three-dimensional representation, Interactive, Spatial Data Exploration, Virtual globe, Development ABSTRACT: This paper describes the concepts and the successful prototypal implementation of interactively connected 2D information visualizations and data displays in 3D virtual environments for the interactive exploration of spatial data and information. Virtual globes or earth viewers such as Google Earth have become very popular over the last few years. They are used for looking at holiday destinations but more importantly also for scientific visualizations. From a geovisualization point of view we might regard 3D data or information displays as yet another representation type that adds to the multitude of information visualization methods. Combining 3D views of data sets with traditional 2D displays offers the advantage of being able to use 3D if and when this type of representation is considered useful or effective for finding new insights into a data set. The traditional and newer displays of mainly 2D information visualization may be enhanced and new insights into the data may be generated by displays of the data in a 3D virtual environment. On the other hand, data in 3D displays might be better understood by simultaneously reading and querying connected 2D representations.The paper presents a prototypal implementation of the interactively connected visualizations of spatial information in 2D views and 3D virtual environments using the brushing technique. -
Graph Visualization and Navigation in Information Visualization 1
HERMAN ET AL.: GRAPH VISUALIZATION AND NAVIGATION IN INFORMATION VISUALIZATION 1 Graph Visualization and Navigation in Information Visualization: a Survey Ivan Herman, Member, IEEE CS Society, Guy Melançon, and M. Scott Marshall Abstract—This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, state–transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective. Index Terms—Information visualization, graph visualization, graph drawing, navigation, focus+context, fish–eye, clustering. involved in graph visualization: “Where am I?” “Where is the 1 Introduction file that I'm looking for?” Other familiar types of graphs lthough the visualization of graphs is the subject of this include the hierarchy illustrated in an organisational chart and Asurvey, it is not about graph drawing in general. taxonomies that portray the relations between species. Web Excellent bibliographic surveys[4],[34], books[5], or even site maps are another application of graphs as well as on–line tutorials[26] exist for graph drawing. Instead, the browsing history. In biology and chemistry, graphs are handling of graphs is considered with respect to information applied to evolutionary trees, phylogenetic trees, molecular visualization. maps, genetic maps, biochemical pathways, and protein Information visualization has become a large field and functions. Other areas of application include object–oriented “sub–fields” are beginning to emerge (see for example Card systems (class browsers), data structures (compiler data et al.[16] for a recent collection of papers from the last structures in particular), real–time systems (state–transition decade). -
Infovis and Statistical Graphics: Different Goals, Different Looks1
Infovis and Statistical Graphics: Different Goals, Different Looks1 Andrew Gelman2 and Antony Unwin3 20 Jan 2012 Abstract. The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey’s Exploratory Data Analysis (1977) and Tufte’s books in the succeeding decades. But statistical graphics still occupies an awkward in-between position: Within statistics, exploratory and graphical methods represent a minor subfield and are not well- integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) is huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles. We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphics designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization. Recent decades have seen huge progress in statistical modeling and computing, with statisticians in friendly competition with researchers in applied fields such as psychometrics, econometrics, and more recently machine learning and “data science.” But the field of statistical graphics has suffered relative neglect. -
Data Visualization: an Exploratory Study Into the Software Tools Used by Businesses
Journal of Instructional Pedagogies Volume 18 Data visualization: an exploratory study into the software tools used by businesses Michael Diamond Jacksonville University Angela Mattia Jacksonville University Abstract Data visualization is a key component to business and data analytics, allowing analysts in businesses to create tools such as dashboards for business executives. Various software packages allow businesses to create these tools in order to manipulate data for making informed business decisions. The focus is to examine what skills employers are looking for in potential job candidates, and compare with the ability to include those technological skills in a business school curriculum. The researchers explored a variety of software tools, and reported their initial results and conclusions. Keywords: data visualization, business analytics, dashboards Copyright statement: Authors retain the copyright to the manuscripts published in AABRI journals. Please see the AABRI Copyright Policy at http://www.aabri.com/copyright.html Data Visualization 1 Journal of Instructional Pedagogies Volume 18 Introduction The visualization and interpretation of data is becoming an important skill in today’s business world. Indeed, the inclusion of data visualizations and dashboards allows executives to make decisions in a short amount of time given the information which is needed (Eigner, 2013). This is a critical part of the evolution of business intelligence to what is now known as the area of business analytics (Negash, 2004). Furthermore, the growing importance of ‘big data’ has created a shortage of employees with data and business analytics skills, with the expectations of a need of close to 1.8 million jobs in these areas (Manyika et al., 2012; Lohr, 2012).