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Automatic Viewpoint Selection for a Visualization I/F in a PSE
Machiko Nakagawa, Masami Takata, Kazuki Joe
Nara Women’s University
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Outline
Background
Explain the Viewpoint Entropy
Proposal of View Potential
Experiment
Discussions
Conclusions & Future work
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Background
?
?
y -axis
x-axis
z-axis
data
etc.
time
Importance to select good viewpoints
Problem of viewpoint selectiona lot of visualized information
huge calculation cost of rendering
no criteria for good view
difficult to select good viewpointsNeed enough knowledge of data & visualization technique
Complex object
Large-scale data
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View Selection in PSE
Possible visualization without expertise in PSE.
View selection by user
Eager of automatic viewpoint selectionpossibility of easier visualization
Technique of Automatic Viewpoint Selection with versatility
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Definition of Good Views
No common definition
Local definitions depending on each purpose
Necessary information → visibility
Unnecessary information → invisibility
information
NEED USELESS
Good View
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Previous Works
Vázquez, “Viewpoint Selection Using Viewpoint Entropy“(2001)
A viewpoint definition by information theoryShannon’s Entropy
Viewpoint Entropyprojected Area
the number of visible faces
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Viewpoint Entropy
fN
i t
i
t
i
A
A
A
ApSI
0
log),(
Nf : the number of faces of the scene
Ai : projected area of a face i
A0 : projected area of
the background in open scenes
At : the total projected area of the scene
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Re-experiment of Viewpoint Entropy (1/2)
projected area is moved.
The number of visible faces is constant
As the projected area increases, Viewpoint Entropy increases
best view
Movement of a camera
RE-1
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Re-experiment of Viewpoint Entropy(2/2)
best view
As the number of visible faces increases, Viewpoint Entropy increases
The number of visible faces is increased.
The projected area is almost same as the previous experiment
RE-2
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A Problem of Viewpoint Entropy
The same Viewpoint Entropy value
Difference in information of views
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Improvement of Viewpoint Entropy
Only two properties for viewpoint selection
No other properties which should beBrightness, Color,etc.
plural properties to obtain better views
View Potential
problems of Viewpoint Entropy
Improvement of evaluation method
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Proposal of View Potential
W0 : projected area &
the number of visible faces
W1 : luminance
W2 : chrominance
W3 : weight of objects
3,3,2,2,1,1,0,0
0, **)***( iiiiiii
n
ii WAWAWAWAW
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W1: Luminance(1/2)
Brightness is more sensitive than color difference for human perception
EX) Dark place and/or very small object
Dark picture
Bright picture
Luminance is important for scene recognition.
Recognize shape(brightness)
Unrecognize
color difference
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W1: Luminance(2/2)
Calculation of viewpoint selection with view luminance
YIQ Color System 【 Y(Luminance ) ,I & Q(Chrominance )】
Y = 0.2990 * R + 0.5870 * G + 0.1140 * B
I = 0.5959 * R - 0.2750 * G + 0.3210 * B
Q = 0.2065 * R - 0.4969 * G - 0.2904 * B
convert RGB into YIQ
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Luminance Property
What’s a good view in luminance ?
The value of luminance diffuses.
Larger dispersion in luminance should be selected.
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Difficult
Easy
W2: chrominance
cognition is difference in hue
red-green
yellow-blue RGB Color System
L*a*b*Color System
different impressions by color mapping
chrominance in data
chrominance in perception
bury the difference of
color recognition!
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Chrominance Property
Views with higher space frequency are more recognizable.
The use of a differentiation filter
edge
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W3:Weight objects
Weight each object as the importance degree
The weight of unnecessary objects is 0Reduction of calculation cost
No Need
weight:0
weight:2
Need
weight:1
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Visualization Pipeline (1/2)
vtkRender
vtkRenderWindow
vtk3DSImporter
BYU Data
3DS Data
vtkPolyDataMapper
vtkCubeSource
vtkActor
vtkPolyDataMapper
vtkPolyDataNomals
vtkActor
vtkBYUReader
Create Scene
* Generate a Scene *The polygon object is set up
in vtkRenderWindow
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Pipeline of visualization(2/2)
vtkRenderWindow
vtkActorCollection
vtkActor
vtkTriangleFilter
vtkMassEntropy
GetInformation
NULL
ActorList
Calculate Entropy
take out an Actor of the scene.
calculate each object.
Implemented library
To use vtkMassEntropy
the cell of the polygon is
normalized. calculate information
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Input the weight of each object
Input data necessary for calculation
Calculate contrast
Calculate chrominance
Calculate the Viewpoint Entropy
vtkMassEntropy
vtkMassEntropy
Functions
GetEntropy()
GetCont(vktRenderer)
GetChromi()
SetActors(vtk ActorCollection)
SetWeight(int)
SetInput(vtkPolyData);
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Viewpoint Entropy+ Luminance
Add the property of brightness to RE-1
entropy entrpy + luminance
Select asymmetry and a contrasty view
RE-3
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Experiment of Chrominance : Data description
・ Height: Latitude
・Width: Longitude
・ time: altitude
・ color: temperature
ECMWFThe European Center for Medium-range Weather Forecastsprovide temperature data of the atmosphere.( 1991/1/1)
RE-4
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Comparison of images from experiment results (1/2)
Large deviation Small deviation
High appraisal Low appraisal
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Comparison of images from experiment result (2/2)
Almost same by human vision
High appraisal Low appraisal
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Change hue
The impression changes by hue
Complex temperature change Simple temperature change
High appraisal Low appraisal
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Weighting Objects:Environment
A scene with several objects
A camera moves
with a constant distance around the focus point.
RE-5
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Discussions
luminancecalculating the contrast of the whole scene,
The detail of an object might not be presented.
improvement by the information of color difference
chrominanceNot only the chrominance values but also the chrominance degree based on human perception
application of texture mapping etc.
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Conclusions
An automatic and general viewpoint selection technique is proposed.
View Potential with plural properties is defined.
Experiments with some scenes, and selection of good views
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