Marks and Channels - univie.ac.atvda.univie.ac.at/.../05_marks_and_channels.pdf · – Chapter 5...
Transcript of Marks and Channels - univie.ac.atvda.univie.ac.at/.../05_marks_and_channels.pdf · – Chapter 5...
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Marks + Channels
VisualizationTorsten Möller
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Overview• Marks + channels• Channel effectiveness
– Accuracy– Discriminability– Separability– Popout
• Channel characteristics– Spatial position– Colour– Size– Tilt (angle)– Shape (glyph)– Stipple (texture)– Curvature– Motion 2
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Readings• Munzner, “Visualization Analysis and Design”:
– Chapter 5 (Marks and Channels)• Colin Ware:
– Chapter 4 (Color)– Chapter 5 (Visual Attention and Information that Pops
Out)• The Visualization Handbook:
– Chapter 1 (Overview of Visualization)• Additional (background) reading
– J. Mackinlay: Automating the design of graphical presentations of relational information. ACM ToG, 5(2), 110-141, 1986
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Marks + Channels• Mark: basic graphical element / geometric primitive:
– point (0D)– line (1D)– area (2D)– volume (3D)
• Channel: control appearance (of a mark)– position– size– shape– orientation– hue, saturation, lightness– etc. 4
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According to Bertin ...
Position
Size
(Grey)Value
Texture
Color
Orientation
ShapeSemiology of Graphics [J. Bertin, 67]
Points Lines AreasMarks
Cha
nnels
5
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Stolte / Hanrahan
“Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases”, Chris Stolte and Pat Hanrahan
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Progression
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© Munzner/Möller Based on slide from Mazur
Channel types: Where / What
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What vs. How Much channels• What: categorical
– shape– spatial region– colour (hue)
• How Much: ordered (ordinal, quantitative)– length (1D)– area (2D)– volume (3D)– tilt– position– colour (lightness) 9
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Mark types
• tables: item = point• network: node+link• link types:
– connection: relationship btw. two nodes– containment: hierarchy
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Expressiveness + Effectiveness
• expressiveness principle:– visual encoding should express all of, and
only, the information in the dataset attributes– lie factor
• effectiveness principle:– importance of the attribute should match the
salience of the channel– data-ink ratio
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Effectiveness of Mappings
• Effectiveness according to neurophysiology
• Cells in Visual Areas 1 and 2 differentially tuned to each of the following properties:– Orientation and size (with luminance)– Color (two types of signal)– Stereoscopic depth– Motion
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[Mackinlay, Automating the Design of Graphical Presentations of Relational Information, ACM TOG 5:2, 1986]
Mackinlay’s Retinal Variables
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Effectiveness -- Accuracy
• perceptualjudgementvs. stimulus
• Weber’s law: S = In
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Effectiveness -- Discriminability
• how many colours can I tell apart?
• how many levels of grey etc.
• Ex: line width
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Effectiveness -- Separability
• separable vs. integral channels
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According to Ware ...• Integral display dimensions
– Two or more attributes perceived holistically
• Separable dimensions– Separate judgments about each
graphical dimension• Simplistic classification, with a large
number of exceptions and asymmetries
More integral coding pairs
More separable coding pairs
[C. W
are,
Info
rmat
ion
Visu
aliza
tion]
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Popout - Preattentive processing
• parallel (visual processing)
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Overview• Marks + channels• Channel effectiveness• Channel characteristics
– Spatial position– Color
• visual system• color models• color deficiency
– Size– Tilt (angle)– Shape (glyph)– Stipple (texture)– Curvature– Motion 20
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Channels
• Spatial position: most effective for all data types (remember the power of the plane)
• Size: ‘how much’, interacts with others• Shape/Glyph: ‘what channel’• Stipple/texture: less popular today• Curvature• Motion: large popout effect
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Spatial position
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2.05D
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Colour
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Visual System
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The eye and the retina
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Retina detectors• 1 type of monochrome
sensor (rods)– Important at low light
• Next level: lots of specialized cells– Detect edges, corners,
etc.• Sensitive to contrast
– Weber’s law: DL ~ L
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Retina detectors
• 3 types of color sensors - S, M, L (cones)– Works for bright light– Peak sensitivities located at approx. 430nm,
560nm, and 610nm for "average" observer.– Roughly equivalent to
blue, green, and redsensors
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Color Opponency
C. Ware, “Visual Thinking for Design”28
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Color Models
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RGB Color Space
• Additive system• Colors that can be
represented by computer monitors
• Not perceptually uniform
C. Ware, “Visual Thinking for Design”
Red
Green
White
Yellow
Blue
Cyan
Black
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HSL Color Space• Hue - what people
think of color• Saturation - purity,
distance from grey• Lightness - from
dark to light• Not perceptually
uniform
wikipedia.org 31
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Lab Color Space• Perceptually uniform• L approximates
human perception of lightness
• a, b approximate R/G and Y/B channels
• a, b called chroma
CIELAB 1976 32
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Luminance, Saturation, Hue• Luminance
– How-much channel– discriminability: ~2-4 bins – contrast important
• Saturation– How-much channel– discriminability: ~3 bins
• Hue– What channel– discriminability: ~6-12
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Ordered Data• Luminance
• Saturation
• Brightness
• Rainbow is a learned order!
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Thanks to Moritz Wustinger
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Thanks to Moritz Wustinger
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Thanks to Moritz WustingerSmiley based on http://upload.wikimedia.org/wikipedia/commons/b/bd/A_Smiley.jpg
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Thanks to Moritz Wustinger
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Color deficiency
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Model “Color blindness” • Flaw in opponent processing
– Red-green common (deuteranope, protanope)– Blue-yellow possible (tritanope -- most common)– Luminance channel almost “normal”
• 8% of all men, 0.5% of all women• Effect is 2D color vision model
– Flatten color space– Can be simulated (Brettel et. al.)– http://colorfilter.wickline.org– http://www.colblindor.com/coblis-color-blindness-
simulator/ 40
Source: M. Stone
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Color Blindness
DeuteranopeProtanope TritanopeNo M cones No L cones No S cones
Red / green deficiencies
Blue / Yellowdeficiency
41
Source: M. Stone
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Color-Blindness
Normal Protanope Deuteranope Lightness42
Source: M. Stone
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Overview• Marks + channels• Channel effectiveness• Channel characteristics
– Spatial position– Color
• Other channels:– Size– Tilt (angle)– Shape (glyph)– Stipple (texture)– Curvature– Motion 43
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Relativ vs. absolute judgement
• Weber’s law says that everything is relative, i.e. the “intensity” depends on the background signal
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Relativ vs. absolute judgement
• Weber’s law says that everything is relative, i.e. the “intensity” depends on the background signal
46
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Relativ vs. absolute judgement
• Weber’s law says that everything is relative, i.e. the “intensity” depends on the background signal
47
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49http://de.wikipedia.org/wiki/Optische_Täuschung