Tutorial on Visualization

Gitta Domik
University of Paderborn
Paderborn, Germany
domik@siggraph.org

Definition, Goals and History of Visualization
Visualization Concepts
Characterization of Data
On the Perception of Visuals
Useful Hints
Visualization Techniques
Glossary
References

Definition, Goals and History of Visualization

Various Flavors of "Visualization", e.g.


Definitions of Visualization

visualize

"to 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"
[The Oxford English Dictionary, 1989]


Scientific Visualization/Example Definitions

"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." [MCC87]

"Scientific visualization is a new, exciting field of computational science spurred on in large measure by the rapid growth in computer technology, particular in graphics workstation hardware and computer graphics software. [Visualization tools] are beginning to impact our daily lives through usage in the arts, particularly film animation, and they hold great promise for scientific research and education. When computer graphics is applied to scientific data for purposes of gaining insight, testing hypothesis, and general elucidation, we speak of scientific visualization." [ARE94]

"A useful definition of visualization might be the binding (or mapping) of data to a representation that can be perceived. The types of binding could be visual, auditory, tactile, etc. or a combination of these." [FOL94]

"Visualization is more than a method of computing. Visualization is the process of transforming information into a visual form, enabling users to observe the information. The resulting visual display enables the scientist or engineer to perceive visually features which are hidden in the data but nevertheless are needed for data exploration and analysis." [GER94]

"Visualization is analytic graphics". Carol Hunter, LLNL [WWW1]


Scientific Visualization/Synthesis of Definitions

Mapping from computer representations to perceptual (visual) representations, choosing encoding techniques to maximize human understanding and communication


Scientific Visualization/Goals


Visualization and adjacent disciplines

Computer Graphics: Efficiency of algorithms (CG) versus effectiveness of use (V).

Computer Vision: Mapping from pictures to abstract description (CV) versus mapping from abstract description to pictures (V).

Image Processing: Mapping from data domain to data domain (IP) versus mapping from data domain to picture domain (V).

(Visual) Perception: General and scientific explanation of human abilities and limitations (VP) versus goal oriented use of visual perception in complex information presentation.

Art and Design: Aesthetics and style (AD) versus expressiveness and effectiveness (V).


Scientific Visualization/History

Need and opportunity

=> NSF Committee to solve problems

Committee on "Graphics, Image Processing, and Workstations" (1986)

Goal of committee

Result of committee

Solidifying goals

Key Publication: [MCC87]

Visualization Concepts

Current exploitation of information accessible by computer: a fraction ! Future increase of data rates expected

=> Need for systematic strategies (concepts, methodologies, intelligent visualization systems) to exploit data [ROB94]


Two strategies


Use of mapping constraints

Arising from


Data characteristics

Data characteristics include


Interpretation aims

Interpretation aims are defined by the viewer(s), e.g. for


Abilities and desires of user

Restrictions by

[DOM94]


Available software and hardware

Restrictions by


"Meaningful pictures"

Coherent visual representations

Use of appropriate visual attributes (visual cues)


Approaches to systematic strategies: Overview


Selected concepts of visualization systems

Mackinlay (APT)

A Presentation Tool [MAC86]

Automatic 2-d discrete data presentation of relational information


Roth and Mattis (SAGE)

SAGE [ROT90]

Includes components for constructive design of graphics (SageBrush) and retrieval of graphics (SageBook).


Casner (BOZ)

[CAS91]

Approach from task analysis

Operating on relational database to produce 2-d graphics


Senay and Ignatius (VISTA)

VISualization Tool Assistant: extension to 3d visualizations [SEN94]

Knowledge-based system to automatically design visualizations


Robertson (NSP)

Natural Scene Paradigm [ROB91]

=>assures coherency through top-down design of complex scenes

=>assures problem-free interpretation through perceptual skills of humans


Wehrend and Lewis (Catalog of Visualizations)

Classification of simple and complex visualization techniques [WEH90]

Categorize each visualization technique by:

"Catalog of visualization techniques": large 2-d matrix to identify meaningful visualization techniques for a pair of (attribute/operation).


Haber and McNabb (Visualization Idioms) [HAB90]

Visualization process is series of transformations to convert raw simulated data into a displayable image:

Visualization idiom: "a specific sequence of data enrichment and enhancement transformations, visualization mappings and rendering transformations that produce an abstract display of a scientific data set".


Beshers and Feiner (AutoVisual)

Rule-based design of interactive multivariate visualizations (n-Vision) [BES93]


Robertson, Card and Mackinlay (Information Visualizer)

Paradigm to optimize cost structure for finding and accessing information [ROB93]

Information workspaces characterized by

Sample visualization techniques: Cone trees, Perspective Wall, 3D/Rooms

Characterization of data

Data

"data" = information that can be represented in computer readable format

data model = conceptual view of data

data model data format (physical view of data) !

choose expressive/effective visualization technique

avoid "mental road blocks"

[BRO92], [GAL94]


Overview of selected data characteristics

Non-orthogonal characteristics


Nominal, ordinal, quantitative

Priorities of Visual Attribute for Various Data Types (Excerpt) [MAC86]

Quantitative Ordinal Nominal
Position Position Position
Length Density Hue
Angle Saturation Density
Slope Hue Saturation
Area Length Shape
Density Angle Length
Saturation Slope Angle
Hue Area Slope
Shape Shape Area

Point, Scalar, Vector

Syntactical categories, additionally characterized by dimensions


"Continuous" data

"Continuous" data can be represented by (samples of) function:

yi = fi (X), where X = (x1 , x2 , x3 , ..., xn ); i=[1,....,m]
 
x .... independent variables; e.g space, time, spectral ("dimensions")
 
y .... dependent variables ("parameters")
 
x, y large ... multidimensional, multiparameter, multivariate data
 

=> regular/irregular format

Expressive visualizations of functions: similar to scalar, quantitative, ordinal

Interpolation methods: must be meaningful in problem space

Computation time for visualization techniques faster on regular grids


Topology/structure of non-continuous data


Other data characteristics

On the Perception of Visuals

Visualizations/Pictures and Visual Attributes

Visualizations/Pictures

Visual attributes

[BER67], [TUF83], [KEL93]


Interpretation of visual attributes

[GER94][FOS95]


Visual attributes discussed in depth

Color


Hue


Saturation


Brightness


Texture


Orientation


Depth attributes

Use depth attributes to enhance the perception of 3-d structures


Motion

Useful hints

Interaction


A good start to generate visualizations "manually"

Quick-and-dirty strategy:


Annotations

Annotations aid the interpretation of visual attributes

Examples of annotations


"Back to Numbers"

Visualization process involves transformation between various domains

reality (problem domain)

"data" domain

visual domain (objects and their visual attributes)

--> careful with interactive exploration of data: respond with meaningful values

Visualization Techniques


Visualization Techniques

Examples of simple techniques for point and scalar data

Histograms (1-d and 2-d), Pie and Bar charts

Representative data characteristics

Techniques

Reference(s) [BRO92]


Examples of techniques for data of high dimensionality

(n-dimensional) Scatter Plots

Data characteristics: multivariate data space, such as botanical observations

Technique

Effectiveness

Interaction: control over view point, rotation, "rocking"; "conditional box"

Reference: [CRA90]


Glyphs/Icons

Representative data characteristics,

Technique

Special note on effectiveness

Special note on interaction

References

[GOR89], [GRI90], [BED90], [INS94]


N-Vision / Worlds within Worlds

Representative data characteristics

Technique

Special note on effectiveness

Special note on interaction

Reference(s)

[BES94]


Examples of techniques for 1-dimensional (continuous) scalar data

Line Graphs

Representative data characteristics

Technique

Special note on effectiveness

Reference(s) [BRO92]


Contour Lines (Isolines)

Representative data characteristics

Technique

Special note on effectiveness


Surface View

Representative data characteristics

Wireframe technique

Shaded surface technique

Special note on effectiveness


Bump Map

Representative data characteristics

Technique

Special note on effectiveness

Reference(s) [KEL93]


Image Display

Representative data characteristics

Technique

Special note on effectiveness


Color Transformations

Representative data characteristics

Technique

Special note on effectiveness


Examples of techniques for 3-dimensional (continuous) scalar data

Volume Slices

Representative data characteristics

Technique

Special note on effectiveness


Basket Weave

Representative data characteristics

Technique

Special note on effectiveness

Reference [SEW88]


Surface Rendering

Representative data characteristics

Technique

Reference [KAU91]


Volume viewing

Representative data characteristics

Technique

Special note on effectiveness

Reference [KAU91], [KAU94]


Tiny Cubes

Representative data characteristics

Technique

Special note on effectiveness

Reference [NIE90]


Examples of techniques for vector data

Arrows

Representative data characteristics

Technique

Special note on effectiveness

Reference(s) [POS94]


Particle traces and motion, streamlines, stream ribbons and surfaces

Representative data characteristics

Techniques

Special note on interaction

Reference(s) [POS94], [HEL94]


Examples of techniques for information visualization

Perspective Wall

Representative data characteristics

Technique

Special note on effectiveness

Special note on interaction

Reference(s)


Cone Trees

Representative data characteristics

Technique

Special note on interaction

Reference(s) [ROB93]


Examples of techniques for software visualization

Scalar Mapping Technique

Representative data characteristics

Technique

Special note on interaction

Reference(s): [HIB92] for VIS-AD, [KIM94] for PV


Program Flow Diagrams

Visual Programming

Representative data characteristics

Technique

Special note on interaction

Reference(s): e.g. AVS, Khoros, SGI Explorer, apE


Ball-and-Stick Technique

Representative data characteristics

Technique

Special note on effectiveness

Special note on interaction

Reference(s)

[KEL93], [FOL94]


Animation

Representative data characteristics

Technique

Special note on effectiveness

Reference(s) [BRY94], [THA94]; for algorithm visualization see [BRO84]


Examples of techniques for special purposes

Glossary

animation
A movie. A sequence of related images viewed in rapid succession to see and experience the apparent movement of objects. [KEL93]

back-to-front
A volume viewing algorithm in which the traversal for viewing is performed from the farthest voxel backward to the closest one. Voxels nearer to the viewer overwrite voxels that are farther to the back. [KAU91]

brightness
The apparent intensity of light. Often a synonym for intensity [KEL93]

contour plots
A technique for plotting scalar data of the form f(x,y) by constructing closed (level) curves of equal values of f. [WOL93]

effectiveness
An effective graph presents all information clearly in view of visualization aims. [MAC86]

expressiveness
An expressive graph encodes all relevant information and only that information. [MAC86]

front-to-back
A volume viewing algorithm in which the traversal for viewing is performed from the voxel closest to the viewer to the farthest one. Voxels are written only to pixels that are not painted yet. [KAU91]

glyph
An object or symbol for representing data values. Glyphs are generally a way of representing many data values and are sometimes called icons. A common glyph is the arrow, often chosen to represent vector fields. The arrow depicts both speed and direction at a point. [KEL93]

Grand Tour
The grand tour is a method for viewing multivariate statistical data via orthogonal projections onto a sequence of two-dimensional subspaces. The sequence of subspaces is chosen so that it is dense in the set of all two-dimensional subspaces. Desirable properties of such sequences of subspaces are considered, and several specific types of sequences are tested for rapidity of becoming dense. Tabulations are provided of the minimum length of a grand tour sequence necessary to achieve various degrees of denseness in dimensions up to 20. [ASI85]

HLS (hue,lightness,saturation)
This color model is defined in the double-cone subset of a cylindrical space. Hue (H) is measured by the angle around the vertical axis, with red at 0 degree, green at 120 degree and so on. The height of the cone represents lightness (L) in a range from 0 (black) at the apex of the first cone to 1.0 (white) at the apex of the second one. Saturation (S) is a ratio ranging from 0 on the center line (V axis) to 1 on the triangular sides of the cones.

HSV (hue,saturation,value)
This colour model is user-oriented, being based on the intuitive appeal of the artist's tint, shade and tone. The coordinate system is cylindrical and the HSV model is defined as a cone within this cylinder. Hue (H) is measured by the angle around the vertical axis, with red at 0 degree, green at 120 degree and so on. The height of the cone represents value (V) in a range from 0 (black) at the apex to 1.0 (white) at the base of the cone. Saturation (S) is a ratio ranging from 0 on the center line (V axis) to 1 on the triangular sides of the cone.

icons
See glyphs.

intensity (of color)
The amount of measured light energy. Often a synonym for brightness. [KEL93]

interactive
Describes behavior of the computer and program designed to respond to the user's request in a timely manner, generally a few seconds or milliseconds. [KEL93]

interpolation
The process of computing new intermediate data values between existing data values. [KAU91]

interpretation aims
User's goals when interpreting picture, such as identifying objects, comparing values of objects, distinguishing objects, focusing on certain details in text

isosurface
Surfaces within a volume that have the same parameter value. [WOL93]

Marching Cubes
A method of visualizing 3-D data structures by looking for level surfaces in a 3-space comprised of a lattice of points. In contrast to volume rendering, where one can see the entire structure, marching cubes only allows a single surface to be rendered. [WOL93]

nominal data types
are unordered collections of symbolic names without units. For instance, the names of the orbiters, such as Hubble, Magellan, Mariner, Viking and Voyager form a nominal data set.

opacity
A material property that prevents light from passing through the object. [KAU91]

ordinal data types
are rank-ordered only, where the ordering does not reflect the magnitudes of the differences. A typical example of an ordinal data set is the sequence of names of the calendar months, January through December.

pixel
Equivalently, picture element, a pixel is the smallest unit of a computer image and is assigned a unique color after rendering. [WOL93]

quantitative data types
are usually expressed as REAL values in the data set. The precise numerical value has a certain importance in the semantics of the data. have concrete values like reals . A typically quantitative data set is the length of objects.

radiosity
In Computer Graphics, the rate at which light energy leaves a surface, which includes transmission and reflection. Rendering techniques which compute the radiosity of all surfaces in a scene have been termed radiosity methods. [WOL93]

ray-casting
A volume viewing algorithm in which sight rays are cast from the viewing plane through the volume. The tracing of the ray stops when the visible voxels are determined by accumulating or encountering an opaque value. [KAU91]

ray-tracing
The general technique of computing an image by projecting rays into a scene and using their interactions with the contents of that scene to determine pixel colors. In surface- rendering methods, rays are intersected and possibly reflected or refracted by objects in the scene to determine visible colors. Ray-tracing is also used in volume visualization and is a type of DVR. [WOL93]

render
The process of converting the polygonal or data specification of an image to the image itself, including color and opacity information. [WOL93]

renderer
A software algorithm which renders an image, calculating a color at each pixel based on object visibility and lighting and shading models. [WOL93]

RGB
Red-green-blue, the color standard employed by the most computer manufacturers and which roughly corresponds, in frequency, to the three bands of colors sensitivity of the human eye. [WOL93]

scalar data types
possess a magnitude, but not directional information other than a sign; they are simply defined as single numbers. Same as quantitative data types in this text.

surface modeling
Techniques and tools for building up computer representations of objects by modeling their surfaces, usually as a collection of polygonal facets. [WOL93]

surface rendering
an indirect technique used for visualizing volume primitives by first converting them into an intermediate surface representation (see surface reconstruction) and then using conventional computer graphics techniques to render them. [KAU91]

surface reconstruction
A procedure that converts a set of data points or cross sections into a surface representation by identifying the surface and representing it with geometric surface primitives. The reconstruction procedure may use one of several techniques, such as contouring, tiling, marching cubes, surface detection, and surface tracking. [KAU91]

thresholding
A technique used primarily with surface rendering, in which a density value of the interface between two materials in the dataset is selected so that the interface surface can be identified for rendering. [KAU91]

translucency
describes the property that allows light to partially pass through and partially reflect. Translucency has the effect of making the translucent area appear smoky or cloudlike, thus revealing objects behind. [KEL93]

transparency
A material property that allows light to pass through the object. [KAU91]

vectors
have direction and magnitude. Quantitatively, their mathematical presentation requires a number of scalar components equal to the dimensionality of the coordinate system. In general, a vector is a unified entity, which implies the problem of displaying independent, multivariate scalar fields.

visualization idiom
is a specific sequence of data enrichment and enhancement transformations, visualization mappings and rendering transformations that produce an abstract display of a scientific data set [HAB90]

volume rendering
Volume rendering is a direct technique for visualizing volume primitives without any intermediate conversion of the volumetric dataset to surface representation. [KAU91]

volume viewing
The process of projecting the volumetric dataset onto the image space by determining which voxels are visible and what their contribution to the final image is. [KAU91]

volume visualization
Volume visualization is a visualization method concerned with the representation, manipulation, and rendering of volumetric data. [KAU91]

volumetric dataset
A volumetric dataset is represented as a 3D discrete regular grid of volume elements (voxels) and is commonly stored in a volume buffer (or cubic frame buffer, like frame buffer in 2D), which is a large 3D array of voxels. [KAU91]

volumetric graphics
Volume graphics is the subfield of computer graphics concerned with volume synthesis, volume modeling and volume visualization, typically using a cubic frame buffer to store the volumetric dataset. Volumetric graphics is the 3D conceptual counterpart of raster graphics. [KAU91]

voxel
An abbreviation for "volume element" or "volume cell." It is the 3D conceptual counterpart of the 2D pixel. Each voxel is a quantum unit of volume and has a numeric value (or values) associated with it that represents some measurable properties or independent variables of the real objects or phenomena. [KAU91]

References

[ARE94], H. Aref, R. D. Charles and T. T. Elvins, Scientific Visualization of Fluid Flow, in C.A. Pickover and S.K. Tewksbury (eds), Frontiers of Scientific Visualization, 1994, Wiley Interscience.

[ASI85] D. Asimov, The Grand Tour: A Tool for Viewing Multidimensional Data. SIAM J. Sci. Statistical Computing, Vol. 6, No.1, 1985, pp. 128-143.

[BAE86] R. M. Baecker, 1986, An Application Overview of Program Visualization. Computer Graphics: SIGGRAPH '86, 20 (4):325, July 1986

[BED90] J. Beddow, 1990, Shape Coding for Multidimensional Data on a Microcomputer display. Proc. of IEEE Conf. VISUALIZATION '90, pp. 238-246.

[BER67] J. Bertin (1967), Semiologie Graphique, Gauthier-Villars, Paris.

[BES93] C. Beshers and Feiner, S., 1993, AutoVisual: Rule-based design of interactive multivariat visualizations. IEEE Computer Graphics and Applications. 13 (4), pp. 41-49.

[BES94] C. Beshers and S. Feiner, Automated Design of Data Visualization, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[BRO84] M.H. Brown and R. Sedgewick, A System for Algorithm Animation, Computer Graphics, Vol. 18, No. 3, July 1984.

[BRO92] K.W. Brodlie, L.A. Carpenter, R.A. Earnshaw, J.R. Gallop, R.J. Hubbard, A.M. Mumford, C.D. Osland, P. Quarendon (eds), Scientific Visualization, Techniques and Applications, 1992, Springer-Verlag.

[BRY94] S. Bryson, Real-Time Exploratory Scientific Visualization and Virtual Reality, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[CAS91] Stephen M. Casner, A Task-Analytic Approach to the Automated Design of Graphic Presentations, ACM Trans Graphics, Vol. 10, No. 2, April 1991, Pages 111-151.

[CRA90] Stuart L. Crawford and Thomas C. Fall, 'Projection Pursuit Techniques for Visualizing High-Dimensional Data Sets', in Visualization in Scientific Computing, G. M. Nielson, B. Shriver and L.J. Rosenblum (eds), IEEE Computer Society Press.

[DOM93] Domik, G., 1993, A Paradigm for Visual Representations, University of Colorado, Department of Computer Science, Boulder, CO. 80309-0430, USA.

[DOM94] G. Domik and B. Gutkauf, 1994, User Modeling for Adaptive Visualization Systems, Proceedings of IEEE Visualization '94, IEEE Computer Society Press, pp. 217-223.

[FOL94] J. Foley and B. Ribarsky, Next-generation Data Visualization Tools, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[FOS95] L.D. Fosdick, E.R. Jessup, C. J.C. Schauble, and G. Domik, An Introduction to High-Performance Scientific Computing, to appear by MIT Press in December 1995.

[GAL94]J. Gallop, Underlying Data Models and Structures for Visualization, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[GER94] N. Gershon, From Perception to Visualization, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[GOR89] I. E. Gordon, Theories of Visual Perception, 1989, John Wiley & Sons.

[GRI90] G. Grinstein and S. Smith (1990), The Perceptualization of Scientific Data in Proc. SPIE (Int. Soc. Opt. Eng.) Extracting Meaning from Complex Data: Processing, Display, Interaction, Santa Clara, CA, 1259: 190-0

[HAB90] R.B. Haber, and D. A. McNabb, Visualization Idioms: A Conceptual Model for Scientific Visualization Systems, in Visualization in Scientific Computing, G. M. Nielson, B. Shriver and L.J. Rosenblum (eds), IEEE Computer Society Press.

[HEL94] James Helman and Lambertus Hesselink, Representation and Display of Vector Field Topology in Fluid Flow Data Sets, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann, Academic Press.

[HIB92] William Hibbard, Charles R. Dyer and Brian Paul,'Display of Scientific Data Structures for Algorithm Visualization', IEEE Proceedings in Visualization 92, pp. 139-146

[INS94] A. Inselberg (panel chair), G. Grinstein, T. Mihalisin, H. Hinterberger, A. Inselberg (panelists), 1994, Visualizing Multidimensional (Multivariate) Data and Relations, Proc. of IEEE Conf. VISUALIZATION '94, pp. 404-409.

[KAU91] Arie Kaufman, 1991, Volume Visualization, IEEE Computer Society Press Tutorial.

[KAU94] A. Kaufmann, Trends in Volume Visualization and Volume Graphics, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[KEL93] P. Keller and M. Keller, Visual Cues, 1994, IEEE Computer Society Press.

[KIM94] D. Kimelman, B. Rosenburg, T. Roth, Strata-Various: Multi-Layer Visualization of Dynamics in Software System Behavior, in IEEE Proceedings Visualization 94.

[MAC86] J. Mackinlay, 1986, Automating the Design of Graphical Presentations of Relational Information, ACM Trans. on Graphics, Vol. 5, No. 2, April 1986, pp 110-141.

[MCC87] McCormick, B.H., T.A. DeFanti, M.D. Brown (ed), Visualization in Scientific Computing, Computer Graphics Vol. 21, No. 6, November 1987

[NIE90] Nielson Gregory M, Hamann B.,'Techniques for the Interactive Visualization of Volumetric Data', IEEE Proceedings in Visualization 90, pp. 45-49.

[POS94] Frits H. Post and Jarke J. van Wijk, 1994, Visual representation of vector fields: recent developments and research directions, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann, Academic Press.

[ROB91] Robertson, P.K. , 1991, A Methodology for Choosing Data Representations, IEEE Computer Graphics and Applications, Vol. 11, No. 3, May 1991, pp. 56-68.

[ROB93] George G. Robertson, Stuart K. Card, and Jock D. Mackinlay,'Information Visualization Using 3D Interactive Animation', CACM, Vol. 36, No. 4, April 1993.

[ROB94]P. Robertson and L. De Ferrari, Systematic Approaches to Visualization: Is a Reference Model Needed? in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[ROT90] Roth, S. and Mattis, J., 1990, Data characteristization for intelligent graphics presentation. In Proceedings CHI '90, April, pp. 193-200. ACM Press.

[SEN94] H. Senay and E. Ignatius, A Knowledge-Based System for Visualization Design, IEEE Computer Graphics and Applications, November 1994, pp. 36-47.

[SEW88] Sewell, 1988,'Plotting Contour Surfaces', ACM Transactions on Mathematical Software, Vol 14, (1), Mar 1988.

[STA92] J. T. Stasko and Ch. Patterson, 1992, Understanding and Characterizing Software Visualization Systems, Proc. of 1992 IEEE Workshop on Visual Languages, IEEE Computer Society Press.

[THA94] Nadia M Thalmann and Daniel Thalmann, Computer animation: a key issue for time visualization, in Scientific Visualization, 1994, Advances and Challenges, Ed: L. Rosenblum, R.A. Earnshaw, J. Encarnacao, H. Hagen, A. Kaufman, S. Klimenko, G. Nielson, F. Post, D. Thalmann , Academic Press.

[TUF83] E.R. Tufte (1983), The Visual Display of Quantitative Information, Graphic Press, Cheshire, Conn.

[WEH90] S. Wehrend and C. Lewis, A problem-oriented classification of visualization techniques. In Proceedings IEEE Visualization '90, pp. 139-143.

[WOL93] R.S. Wolff and L. Yeager, Visualization of Natural Phenomena, 1993, Springer Verlag (TELOS).

[WWW1] http://web/msi.umn.edu/WWW/SciVis/whatisviz.html


HyperVis Table of Contents

Last modified onMarch 29, 1999, G. Scott Owen, owen@siggraph.org