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A Quick Guide to Visualization. Yingcai Xiao. Computation with and without Visual Assistance. 67 x 89 = ? . Visualized Data Analysis. 67 x 89 --------- 603 + 536 --------- 5963. Visualization. Representing information (data) as
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A Quick Guide to Visualization Yingcai Xiao
Computation with and without Visual Assistance 67 x 89 = ?
Visualized Data Analysis 67 x 89 --------- 603 + 536 --------- 5963
Visualization Representing information (data) as computer graphics.
Scientific, Engineering and Information Visualization Scientific Visualization: Scientific Data Engineering Visualization: Measurement Data Information Visualization: Abstract Data
Scientific Visualization Started from CFD (Computational Fluid Mechanics) in the 80s. Formalized as an research discipline in 1989. (NSF Report on Scientific Visualization).
Scientific Data Commonly in the form of a grid: data values are known on the grid nodes.
Scientific Visualization: Fundamentals Visualizing data variation through out the volume of interest.
Scientific Visualization: Fundamentals Local Trilinear Interpolation
Scientific Visualization: Techniques: Color Mapping • Mapping data values to colors with a color map.
Scientific Visualization: Techniques: Color Mapping A color map.
Scientific Visualization: Techniques: Cut-Away • Revealing data values inside the volume of interest.
Scientific Visualization: Techniques: Slicing • Revealing data values on cutting planes.
Scientific Visualization: Techniques: Iso-surfacing • Iso-surface: a surface of constant data values.
Scientific Visualization: Techniques: Iso-Lines • Iso-line: a line of constant data values.
Scientific Visualization Data Types
Scientific Visualization: Data Types Scalar: one value per data point Vector: 3 values per data point 3 Scalars Tensor: 9 values per data point 9 Scalars 3 Vectors
Vector Visualization 3 scalar values, (vx, vy, vz) => direction and length
Vector Visualization: Directed Lines 3 scalar values, (vx, vy, vz) => direction and length
Vector Visualization: Warping Warping: deformation of geometry according to a vector.
Vector Visualization: Displacement Plots Displacement Plots: represent data values as the displacement of a surface in the direction perpendicular to the surface.
Vector Visualization: Streamlines Streamlines: outlines of fluid flow
Vector Visualization: Streamtubes Stream-tubes: streamline + isosurface + color mapping
Tensor Visualization: Tensor Ellipsoid Three eigenvectors: V1 V2 V3
Scientific Visualization: Mature W. Shroeder, K. Martin, & B. Lorensen The Visualization Toolkit - An Object-oriented Approach to 3D Graphics, 2nd ed. www.kitware.com
Engineering Visualization Intelligent Monitoring Traffic Assembly Line
Intelligent Monitoring • Data capturing • Data analysis • Data representation
Intelligent Monitoring • Data capturing • sensors, video cameras, tracking devices • Data analysis • video image processing is a challenge • Data representation • color coding (e.g. GIS – Geographical Information Systems, google map)
Intelligent Monitoring video image processing : computer vision : OpenCV http://opencv.willowgarage.com/wiki/ http://sourceforge.net/projects/opencvlibrary/ ITK: http://www.itk.org/
Engineering Visualization Measurement Data: Scattered Sparse
Scattered Data: sample points distributed unevenly and non-uniformly throughout the volume of interest.
Engineering Visualization: Two-Step Approach T. Foley & A. D. Lane Visualization of Irregular Multivariate Data Proceedings of the First IEEE Conference on Visualization, San Francisco, CA, 1990
__________ ____________ __ ___ _______ Modeling Rendering Scattered Data Intermediate Grid Rendered Volume Interpolation Grid-based
Interpolation Methods G. M. Nielson Scattered Data Modeling IEEE Computer Graphics & Applications, 13(1), 1993
Interpolation Methods (Nielson, 1993) Global: all sample points are used to interpolated a grid value.Exact: the interpolation function can exactly reproduce the data values on the sample points.
Global Exact Interpolation Functions(Foley & Lane, 1990; Nielson, 1993) n sample points: (xi,yi,zi,vi) for i = 1,2,..nTo define an interpolation function: e.g., thin-plate spline, di is the distance between sample point i and the point to be interpolated p(x,y,z).di = ((x-xi)2+(y-yi )2+(z-zi )2)1/2bi,c1,c2,c3,c4 are n+4 constants to be solved by enforcing the following conditions:f (xi,yi,zi) = vi for i = 1,2,..n
Global Exact Interpolation Functions(Foley & Lane, 1990; Nielson, 1993) Thin-plate spline Volume Spline Multiquadric Shepard
Two-step Approach: Problems Yingcai Xiao, John Ziebarth, Chuck Woodbury, Eric Bayer, Bruce Rundell, Jeroen Zijp “The Challenges of Visualizing and Modeling Environmental Data” IEEE Visualization 96, Conference Proceeding, San Francisco, California, October 1996
Scattered Data: sample points distributed unevenly and non-uniformly throughout the volume of interest.
Deficiencies of the Interpolation-based Two-step Approach • Misinterpretation (negative concentration) • Ambiguity in selecting interpolation methods • Inconsistent interpolations in modeling and rendering • Visualizing secondary data instead of the original data • No error estimation • Unable to add known information • Not efficient
Information Visualization • Data abstract • Not interpolatable • Domain dependent http://en.wikipedia.org/wiki/Information_visualization For examples: Database Visualization (RbDbVis.ppt) Visual Analytics (IA: Intelligence Amplification)