1. Basic Vision+
The process and what stands
out
CH1 – CH2 + supplimentary
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2. 2D Scalar
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3. Arctic Sea Ice Volume (1D
Scalar)
17.5K
~3K
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4. Data Classes
 Scalar Field
• 1D
• 2D
• 3D
 Vector Field
• 1D
• 2D
• 3D
 Time Varying
• All of the above
What is a map?
 Discrete
• 1D
• K-dimensional
 Nominal
• Overlapping sets of data
with multiple nominal
attributes
 Graph G = {V,E}
•
•
•
•
Tree
Directed acyclic
Weighted graph
Hyper graph
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5. Human Visual Field
100
80
60
40
20
LEFT
RIGHT

6. Visual Angle
θ
r
d
h

7. 1 diopter: a lens that focuses at 1 meter.
45d Cornea
~15d Lens
d = 1/f

8. Acuities
Vernier super acuity (10 sec)
Grating acuity
Two Point acuity (0.5 min)
What is the ideal resolution

9. CAVE
iPhone
960x640 5 cm
1280x1280
 Resolution problems
 Light scattering problems

10. CAVE
iPhone
960x640 5 cm
128 pixels/degree
1280x1280
16,000 pixels/ deg2
14 pixels/deg
196 pixels/ deg2
 Resolution problems
 Light scattering
problems

11. Immersion VR
 HMD + head tracking
 Data glove

12. Human Spatial Acuity

13. Cutoff at 50 cycles/deg.
Receptors: 20 sec of arc
Pooled over larger and larger areas
100 million receptors
1 million fibers to brain
A desktop screen may have 30 pixels/cm
– need about 4 times as much.
 I phones better.
 VR displays have 5 pixels/cm






14. Aliasing and Anti aliasing
Input pattern
Pixel matrix
Output pattern

15. Temporal Aliasing
 Human Flicker fusion 50 Hz
 Temporal aliasing occurs with moving
targets
 Must compute motion blur to fix the
problem

16. Acuity Distribution
10
0
80
60
40
20
50
30
10
10
30
D
istance fromFovea (deg.)
50

17. Brain Pixels

18. Ware:Vislab:CCOM

20. Ware:Vislab:CCOM

21. Visual search
U fu V al
se l isu
F ldo V
ie f iew
V a
isu l
Sa o
e rch r
M n rin
o ito g
S te y
tra g
Ee
y
Mv m t
o e en
Cn l
o tro

22. The Process of Visual Thinking
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23. Spotfire product

24. What we can easily find :CH2
 Visual search depends on
• Salience (low level properties)
• Tuning of low level properties
• Search skills (knowledge)
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25. V1 processing
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27. Pre-Attentive Processing
89097909
90949794
7520668
3775252
08032880
80808508
0808002
25809-8
548t8050
678y2970
7274047
8925272
29849628
18950078
7405492
83r7591
89789490
94952579
7705579
5928932

28. Color is Pre-Attentive (Pops out)
8 9 0 9 72 5 2
9 0 9 4 9 70 9
7 5 2 0 6 6 84
3775 9 9
0 8 0 30 9 - 8
8 0 8 0 8 58 0
0 8 0 8 0 0 28
258 28 0
5 4 8 t 82 7 2
6 7 8 y 2 95 0
7 2 7 4 0 4 70
8925 0 7
2 9 8 4 95 9 1
1 8 9 5 0 02 8
7 4 0 5 4 9 28
83r7 6 7
8 9 7 8 99 399
9 4 9 5 2 59 0
770557 2
5928 4 7

29. Generic Pre-Attentive Experiment
 Number of irrelevant
items varies
 Pre-attentive 10 msec
per item or better.
900
700
500
3
6
12
Number of distractors

30. Color

32. Orientation

34. Motion

36. Size

38. Simple shading

40. Conjunction (does not pop out)

42. Semantic Depth of Field

43. Channels = perceptual
dimensions
Symbol
Coding
Color
Channel
Form
Channel
Motion
Channel
Hue
Sat
Luminance
Size
Orient
Elong
Vertical
Horizontal
Blinking
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44. Relevance
 Symbol design (nominal coding)
 Highlighting, - search guidance.
 Glyph design (quantititive symbols)
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45. Mapping data to display variables
Data glyphs






Position (2)
Orientation (1)
Size (spatial frequency)
Motion (2)++
Blinking?
Color (3)
 Note we have the
problem of heterogeneity
– There is no good
solution
Star glyph
Method

46. Starplot glyph

47. How many dimensions?
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48. Surplus Data into Art?
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49. Symbols
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50. Redundant coding
Outliers on feature dimensions
are more easily seen
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51. A symbol set designed for rapid
search
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52. Asymmetries in search
Add something rather than subtract something
Highlighting methods
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53. Motion Highlighting
We are very sensitive to motion in the
periphery. (Peterson and Dugas, 1972)
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54. How to make a reminder
-emergence (Hillstrom and Yanti, 1994)
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55. Search as a process
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56. Search as a process
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57. Heirarchical design
aids search
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58. The process (again)
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