Transcript Scattered Data Visualization Shanthanand Kutuva Rabindranath Kiran V Bhaskar

Scattered Data
Visualization
Shanthanand Kutuva Rabindranath
Kiran V Bhaskar
Contents
Scattered Data….a brief introduction.
 Data Description.
 Several Possible Approaches.
 Delaunay Triangulation.
 Limitations to visualize scattered data.
 Our Approach-system design
 Implementation Considerations
 Demo.
 Future work.
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Scattered Data-A brief introduction
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Topology & Geometry
Examples- Geophysical and Bio-physical data
Fig: Geophysical Data in 3D space
Data Description
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Three Dimensional Data.
Three columns representing each axes.
Fourth column representing the scalar
value.
Data Normalization.
Several Approaches
 Splatting
 Interpolation
Delaunay Triangulation
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“An optimal triangulation, which satisfies, the
circum-sphere condition “.
Optimal Triangulation: “A triangulation which
generates maximized minimum angles”.
Circum-sphere Condition.
2D-“The minimum interior angle of a triangle in
Delaunay’s triangulation is greater than or equal to
the minimum interior angle of any other possible
triangulation.”
Edge Swapping
Limitations to visualize scattered data
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 Scattered density data can be difficult to visualize, particularly
when the data do not lie on a regular grid.
 It is difficult to visualize scattered data if it contains regions of
sparse measurements. This often occurs in geophysical or biophysical
data.
 Even using Triangulation, if the points are arranged on a
regular lattice(degenerate points), there are several possible
triangulations possible. The choice of triangulation depends on the
order of data input.
 Points that are coincident (or nearly so) may be discarded by
the algorithm. This is because the Delaunay triangulation requires
unique input points. This can be overcome by controlling definition of
coincidence using the “tolerance” instance variable.
System Design
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Data set we have is a 3D dataset
Store it in a double array
Store the concentration value as a scalar value.
A Count Id to keep track of points used.
Implementation Considerations
We are expected to consider the following
conditions before implementing.
 · preserve input data values.
 · produce meaningful output values.
 · provide error estimations.
 · accept additional constraints.
 · reduce the requirement on the sampling
intensity.
Demo
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Click here
Future Work
We wish to extend the same to a larger
dataset.
 A high resolution color table
 Visualize the same data using other methods
and compare.
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