The Visualization Handbook

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The Visualization Handbook
The Visualization Handbook.jpg
Author Charles D. Hansen
Christopher R. Johnson
CountryFlag of the United States.svg  United States
Language English
Subject Scientific visualization
Computer graphics
Publisher Elsevier
Publication date
2005
ISBN 978-0-12-387582-2

The Visualization Handbook is a textbook by Charles D. Hansen and Christopher R. Johnson that serves as a survey of the field of scientific visualization by presenting the basic concepts and algorithms in addition to a current review of visualization research topics and tools. [1] It is commonly used as a textbook for scientific visualization graduate courses. [2] [3] It is also commonly cited as a reference for scientific visualization and computer graphics in published papers, with almost 500 citations documented on Google Scholar. [4]

Contents

Table of Contents

  1. Overview of Visualization - William J. Schroeder and Kenneth M. Martin
  1. Accelerated Isosurface Extraction Approaches -Yarden Livnat
  2. Time-Dependent Isosurface Extraction - Han-Wei Shen
  3. Optimal Isosurface Extraction - Paolo Cignoni, Claudio Montani, Robert Scopigno, and Enrico Puppo
  4. Isosurface Extraction Using Extrema Graphs - Takayuki Itoh and Koji Koyamada
  5. Isosurfaces and Level-Sets - Ross Whitaker
  1. Overview of Volume Rendering - Arie E. Kaufman and Klaus Mueller
  2. Volume Rendering Using Splatting - Roger Crawfis, Daqing Xue, and Caixia Zhang
  3. Multidimensional Transfer Functions for Volume Rendering - Joe Kniss, Gordon Kindlmann, and Charles D. Hansen
  4. Pre-Integrated Volume Rendering - Martin Kraus and Thomas Ertl
  5. Hardware-Accelerated Volume Rendering - Hanspeter Pfister
  1. Overview of Flow Visualization - Daniel Weiskopf and Gordon Erlebacher
  2. Flow Textures: High-Resolution Flow Visualization - Gordon Erlebacher, Bruno Jobard, and Daniel Weiskopf
  3. Detection and Visualization of Vortices - Ming Jiang, Raghu Machiraju, and David Thompson
  1. Oriented Tensor Reconstruction - Leonid Zhukov and Alan H. Barr
  2. Diffusion Tensor MRI Visualization - Song Zhang, David Laidlaw, and Gordon Kindlmann
  3. Topological Methods for Flow Visualization - Gerik Scheuermann and Xavier Tricoche
  1. 3D Mesh Compression - Jarek Rossignac
  2. Variational Modeling Methods for Visualization - Hans Hagen and Ingrid Hotz
  3. Model Simplification - Jonathan D. Cohen and Dinesh Manocha
  1. Direct Manipulation in Virtual Reality - Steve Bryson
  2. The Visual Haptic Workbench - Milan Ikits and J. Dean Brederson
  3. Virtual Geographic Information Systems - William Ribarsky
  4. Visualization Using Virtual Reality - R. Bowen Loftin, Jim X. Chen, and Larry Rosenblum
  1. Desktop Delivery: Access to Large Datasets - Philip D. Heermann and Constantine Pavlakos
  2. Techniques for Visualizing Time-Varying Volume Data - Kwan-Liu Ma and Eric B. Lum
  3. Large-Scale Data Visualization and Rendering: A Problem-Driven Approach - Patrick McCormick and James Ahrens
  4. Issues and Architectures in Large-Scale Data Visualization - Constantine Pavlakos and Philip D. Heermann
  5. Consuming Network Bandwidth with Visapult - Wes Bethel and John Shalf
  1. The Visualization Toolkit - William J. Schroeder and Kenneth M. Martin
  2. Visualization in the SCIRun Problem-Solving Environment - David M. Weinstein, Steven Parker, Jenny Simpson, Kurt Zimmerman, and Greg M. Jones
  3. Numerical Algorithms Group IRIS Explorer - Jeremy Walton
  4. AVS and AVS/Express - Jean M. Favre and Mario Valle
  5. Vis5D, Cave5D, and VisAD - Bill Hibbard
  6. Visualization with AVS - W. T. Hewitt, Nigel W. John, Matthew D. Cooper, K. Yien Kwok, George W. Leaver, Joanna M. Leng, Paul G. Lever, Mary J. McDerby, James S. Perrin, Mark Riding, I. Ari Sadarjoen, Tobias M. Schiebeck, and Colin C. Venters
  7. ParaView: An End-User Tool for Large-Data Visualization - James Ahrens, Berk Geveci, and Charles Law
  8. The Insight Toolkit: An Open-Source Initiative in Data Segmentation and Registration - Terry S. Yoo
  9. amira: A Highly Interactive System for Visual Data Analysis - Detlev Stalling, Malte Westerhoff, and Hans-Christian Hege
  1. Extending Visualization to Perceptualization: The Importance of Perception in Effective Communication of Information - David S. Ebert
  2. Art and Science in Visualization - Victoria Interrante
  3. Exploiting Human Visual Perception in Visualization - Alan Chalmers and Kirsten Cater
  1. Scalable Network Visualization - Stephen G. Eick
  2. Visual Data-Mining Techniques - Daniel A. Keim, Mike Sips, and Mihael Ankerst
  3. Visualization in Weather and Climate Research - Don Middleton, Tim Scheitlin, and Bob Wilhelmson
  4. Painting and Visualization - Robert M. Kirby, Daniel F. Keefe, and David Laidlaw
  5. Visualization and Natural Control Systems for Microscopy - Russell M. Taylor II, David Borland, Frederick P. Brooks, Jr., Mike Falvo, Kevin Jeffay, Gail Jones, David Marshburn, Stergios J. Papadakis, Lu-Chang Qin, Adam Seeger, F. Donelson Smith, Dianne Sonnenwald, Richard Superfine, Sean Washburn, Chris Weigle, Mary Whitton, Leandra Vicci, Martin Guthold, Tom Hudson, Philip Williams, and Warren Robinett
  6. Visualization for Computational Accelerator Physics - Kwan-Liu Ma, Greg Schussman, and Brett Wilson

See also

Related Research Articles

<span class="mw-page-title-main">IBM OpenDX</span>

OpenDX stands for Open Data Explorer and is IBM's scientific data visualization software. It can handle complex domains along with measured or computed data. The data may be scalar, vector or tensor fields at different points of the object. The points at which data is measured don't have to be equally spaced and not need to be homogeneously spaced. The project started in 1991 as Visualization Data Explorer.

<span class="mw-page-title-main">Scientific visualization</span> Interdisciplinary branch of science concerned with presenting scientific data visually

Scientific visualization is an interdisciplinary branch of science concerned with the visualization of scientific phenomena. It is also considered a subset of computer graphics, a branch of computer science. The purpose of scientific visualization is to graphically illustrate scientific data to enable scientists to understand, illustrate, and glean insight from their data. Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information.

<span class="mw-page-title-main">Visualization (graphics)</span> Set of techniques for creating images, diagrams, or animations to communicate a message

Visualization or visualisation is any technique for creating images, diagrams, or animations to communicate a message. Visualization through visual imagery has been an effective way to communicate both abstract and concrete ideas since the dawn of humanity. Examples from history include cave paintings, Egyptian hieroglyphs, Greek geometry, and Leonardo da Vinci's revolutionary methods of technical drawing for engineering and scientific purposes.

<span class="mw-page-title-main">Volume rendering</span> Representing a 3D-modeled object or dataset as a 2D projection

In scientific visualization and computer graphics, volume rendering is a set of techniques used to display a 2D projection of a 3D discretely sampled data set, typically a 3D scalar field.

Volume is the quantity of space an object occupies in a 3D space.

<span class="mw-page-title-main">Marching cubes</span> Computer graphics algorithm

Marching cubes is a computer graphics algorithm, published in the 1987 SIGGRAPH proceedings by Lorensen and Cline, for extracting a polygonal mesh of an isosurface from a three-dimensional discrete scalar field. The applications of this algorithm are mainly concerned with medical visualizations such as CT and MRI scan data images, and special effects or 3-D modelling with what is usually called metaballs or other metasurfaces. The marching cubes algorithm is meant to be used for 3-D; the 2-D version of this algorithm is called the marching squares algorithm.

<span class="mw-page-title-main">ParaView</span> Scientific visualization software

ParaView is an open-source multiple-platform application for interactive, scientific visualization. It has a client–server architecture to facilitate remote visualization of datasets, and generates level of detail (LOD) models to maintain interactive frame rates for large datasets. It is an application built on top of the Visualization Toolkit (VTK) libraries. ParaView is an application designed for data parallelism on shared-memory or distributed-memory multicomputers and clusters. It can also be run as a single-computer application.

Implicit <i>k</i>-d tree

An implicit k-d tree is a k-d tree defined implicitly above a rectilinear grid. Its split planes' positions and orientations are not given explicitly but implicitly by some recursive splitting-function defined on the hyperrectangles belonging to the tree's nodes. Each inner node's split plane is positioned on a grid plane of the underlying grid, partitioning the node's grid into two subgrids.

<span class="mw-page-title-main">VisIt</span>

VisIt is an open-source interactive parallel visualization and graphical analysis tool for viewing scientific data. It can be used to visualize scalar and vector fields defined on 2D and 3D structured and unstructured meshes. VisIt was designed to handle very large data set sizes in the terascale range and yet can also handle small data sets in the kilobyte range.

Visualization Library (VL) is an open source C++ middleware for 2D/3D graphics applications based on OpenGL 4, designed to develop portable applications for the Microsoft Windows, Linux and Mac OS X operating systems.

<span class="mw-page-title-main">Lawrence J. Rosenblum</span> American mathematician

Lawrence Jay Rosenblum is an American mathematician, and Program Director for Graphics and Visualization at the National Science Foundation.

<span class="mw-page-title-main">Scientific Computing and Imaging Institute</span> Research institute at the University of Utah

The Scientific Computing and Imaging (SCI) Institute is a permanent research institute at the University of Utah that focuses on the development of new scientific computing and visualization techniques, tools, and systems with primary applications to biomedical engineering. The SCI Institute is noted worldwide in the visualization community for contributions by faculty, alumni, and staff. Faculty are associated primarily with the School of Computing, Department of Bioengineering, Department of Mathematics, and Department of Electrical and Computer Engineering, with auxiliary faculty in the Medical School and School of Architecture.

<span class="mw-page-title-main">Voreen</span> Volume visualization library and development platform

Voreen is an open-source volume visualization library and development platform. Through the use of GPU-based volume rendering techniques it allows high frame rates on standard graphics hardware to support interactive volume exploration.

<span class="mw-page-title-main">Christopher R. Johnson</span> American computer scientist

Christopher Ray Johnson is an American computer scientist. He is a distinguished professor of computer science at the University of Utah, and founding director of the Scientific Computing and Imaging Institute (SCI). His research interests are in the areas of scientific computing and scientific visualization.

In scientific visualization, Lagrangian–Eulerian advection is a technique mainly used for the visualization of unsteady flows. The computer graphics generated by the technique can help scientists visualize changes in velocity fields. This technique uses a hybrid Lagrangian and Eulerian specification of the flow field. It is a special case of a line integral convolution.

In scientific visualization a tensor glyph is an object that can visualize all or most of the nine degrees of freedom, such as acceleration, twist, or shear – of a matrix. It is used for tensor field visualization, where a data-matrix is available at every point in the grid. "Glyphs, or icons, depict multiple data values by mapping them onto the shape, size, orientation, and surface appearance of a base geometric primitive." Tensor glyphs are a particular case of multivariate data glyphs.

<span class="mw-page-title-main">Amira (software)</span> Software platform for 3D and 4D data visualization

Amira is a software platform for 3D and 4D data visualization, processing, and analysis. It is being actively developed by Thermo Fisher Scientific in collaboration with the Zuse Institute Berlin (ZIB), and commercially distributed by Thermo Fisher Scientific.

Gordon L. Kindlmann is an American computer scientist who works on information visualization and image analysis. He is recognized for his contributions in developing tools for tensor data visualization.

The IEEE Visualization Conference (VIS) is an annual conference on scientific visualization, information visualization, and visual analytics administrated by the IEEE Computer Society Technical Committee on Visualization and Graphics. As ranked by Google Scholar's h-index metric in 2016, VIS is the highest rated venue for visualization research and the second-highest rated conference for computer graphics over all. It has an 'A' rating from the Australian Ranking of ICT Conferences, an 'A' rating from the Brazilian ministry of education, and an 'A' rating from the China Computer Federation (CCF). The conference is highly selective with generally < 25% acceptance rates for all papers.

<span class="mw-page-title-main">Hanspeter Pfister</span> Swiss computer scientist

Hanspeter Pfister is a Swiss computer scientist. He is the An Wang Professor of Computer Science at the Harvard John A. Paulson School of Engineering and Applied Sciences and an affiliate faculty member of the Center for Brain Science at Harvard University. His research in visual computing lies at the intersection of scientific visualization, information visualization, computer graphics, and computer vision and spans a wide range of topics, including biomedical image analysis and visualization, image and video analysis, and visual analytics in data science.

References

  1. "Description for "Visualization Handbook"". Academic Press. 29 December 2004. Retrieved 5 April 2017.
  2. "Blue Waters Project to Offer Graduate Visualization Course in Spring 2015". Scientific Computing. 18 August 2014. Retrieved 5 April 2017.
  3. Chen, Min. "Visual Analytics". Oxford University Department of Computer Science. Retrieved 5 April 2017.
  4. "Citations for The Visualization Handbook". Google Scholar. 1 January 2011.