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Sean Gustafson master of science thesis defense D ec 15 / 2008. Visualizing Off-Screen Locations on Small Mobile Displays. off-screen viz. overview. arrows. halo. f. rel. ated. work. overviews. [Plaisant 95 and many others]. fisheye views. fisheye views. [Carpendale 01].

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Presentation Transcript
slide1

Sean Gustafson

master of science

thesis defense

Dec 15 / 2008

Visualizing

Off-Screen Locations on

Small Mobile Displays

slide2

off-screen viz

overview

arrows

halo

f

slide3

rel

ated

work

slide4

overviews

[Plaisant 95 and many others]

fisheye views
fisheye views

fisheye views

[Carpendale 01]

slide6

simple arrows

[Tecmo Bowl 87]

slide8

edgeradar

[Gustafson 07]

slide9

halo

[Baudisch 03]

slide10

halo

[Baudisch 03]

slide14

initial approaches

clustering

ovals

Baudisch & Rosenholtz

Irani et al.

cut lines

color

separatevisually

slide15

th

e

wedge

slide16

wedge

base

base

base

base

leg

leg

target

slide17

halo

wedge

3 degrees of freedom
3 degrees of freedom

intrusion

base length

rotation

avoiding overlap

rotation

base length

avoiding overlap

intrusion

slide20

lay

out

algorithm

leaves 2 degrees
leaves 2 degrees

intrusion ~ distance

base-width ~ distance

slide27

u

ser

study

slide29

task 1: locate

same as original halo paper

halo

wedge

slide30

task 2: closest

same as original halo paper

halo

wedge

slide31

task 3: avoid

same as original halo paper

halo

wedge

slide32

IV: density

sparse

dense

slide33

IV: corner

side

corner

slide35

hypotheses

  • 1. wedge is more accurate
  • larger improvement in dense condition
  • larger improvement in corners
  • (no hypothesis about task time)
slide38

error in pixels

Side

80

Halo

Wedge

60

Error (pixels)

40

20

0

Dense

Sparse

locate task

slide39

error in pixels

Corner

Side

80

Halo

Wedge

60

Error (pixels)

40

20

0

Dense

Sparse

Dense

Sparse

locate task

slide40

other results

no interesting results from the other tasks or from completion time.

slide41

other results

preferences

slide42

hypotheses

  • 1. wedge is more accurate
  • larger improvement in dense condition
  • larger improvement in corners

slide43

mod

eling

pointing

slide47

beam

beam

slide48

beam

orbital

beam

slide55

data collection

experiment to gather accuracy data

slide56

data collection

experiment to gather accuracy data

legLength1

distance

baseLength

legLength2

slide57

results

60

50

0

-60

60

100

0

-60

60

Distance

Breadth Error

0

200

-60

60

0

300

-60

-100

0

100

200

300

Depth Error

slide58

results

60

length

width

0

bias

-60

-100

0

100

200

300

Depth Error

slide61

minimizing errors

  • choose smallest base and leg length that still maintains small orbital and low bias
  • base length of at least 50px, 100px for longer distances
  • a large leg length (above 8px) does not result in smaller orbital
  • avoid heavily rotating wedges
slide63

contributions

  • summary of existing techniques and criteria for evaluatingthem
  • introduction of Wedge
  • introduction of the concept of degrees of freedom and dynamic layout
  • evaluation of Wedge
  • preliminary model
slide66

IV: density

1

2

sparse

3

4

dense

slide71

wedge=

uni

fied

pointing

slide72

intrusion  0

citylights

slide73

aperture  0

stretched arrows

slide74

?

halo

slide75

distance information in corner

distance information along arc

round bases

slide78

full results

Corner

Side

Corner

Side

80

Halo

Halo

3000

Wedge

Wedge

60

2000

Time (ms)

1000

Error (pixels)

40

0

Dense

Sparse

Dense

Sparse

20

0

Dense

Sparse

Dense

Sparse

locate task

slide79

full results

Corner

Side

50

Halo

Corner

Side

Wedge

40

Halo

5000

Wedge

30

Error Rate %

4000

20

3000

Time (ms)

10

2000

0

1000

Dense

Sparse

Dense

Sparse

0

Dense

Sparse

Dense

Sparse

avoid task

slide80

full results

Corner

Side

Corner

Side

Halo

Halo

Wedge

Wedge

60

6000

4000

40

Time (ms)

Error Rate %

2000

20

0

0

Dense

Sparse

Dense

Sparse

Dense

Sparse

Dense

Sparse

closest task