Scientific Visualization Melanie Tory Acknowledgments: Torsten - - PowerPoint PPT Presentation

▶
scientific visualization
SMART_READER_LITE
LIVE PREVIEW

Scientific Visualization Melanie Tory Acknowledgments: Torsten - - PowerPoint PPT Presentation

Nov 09, 2022 326 likes •919 views

Scientific Visualization Melanie Tory Acknowledgments: Torsten Mller (Simon Fraser University) Raghu Machiraju (Ohio State University) Klaus Mueller (SUNY Stony Brook) 1 Overview 4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4

slide-1
SLIDE 1

1

Scientific Visualization

Melanie Tory

Acknowledgments: Torsten Möller (Simon Fraser University) Raghu Machiraju (Ohio State University) Klaus Mueller (SUNY Stony Brook)

slide-2
SLIDE 2

2

Overview

4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

slide-3
SLIDE 3

3

Difference between SciVis and InfoVis

Direct Volume Rendering Streamlines Line Integral Convolution Glyphs Isosurfaces

SciVis

Scatter Plots Parallel Coordinates Node-link Diagrams

InfoVis

[Verma et al., Vis 2000] [Hauser et al., Vis 2000] [Cabral & Leedom, SIGGRAPH 1993] [Fua et al., Vis 1999] [http://www.axon.com/ gn_Acuity.html] [Lamping et al., CHI 1995]

slide-4
SLIDE 4

4

Difference between SciVis and InfoVis

4 Card, Mackinlay, & Shneiderman:

– SciVis: Scientific, physically based – InfoVis: Abstract

4 Munzner:

– SciVis: Spatial layout given – InfoVis: Spatial layout chosen

4 Tory & Möller:

– SciVis: Spatial layout given + Continuous – InfoVis: Spatial layout chosen + Discrete – Everything else -- ?

slide-5
SLIDE 5

5

Overview

4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

slide-6
SLIDE 6

6

Medical Scanning

4

slide-7
SLIDE 7

7

Medical Scanning - Applications

4 4

slide-8
SLIDE 8

8

Medical Scanning - Applications

4 4 4 !

!

slide-9
SLIDE 9

9

Biological Scanning

4 " # 4 $ %

&

slide-10
SLIDE 10

10

Industrial Scanning

4 '( 4 ) 4 '(

slide-11
SLIDE 11

11

Scientific Computation - Domain

4 4 *+,+'

  • (

4 .'.( 4

slide-12
SLIDE 12

12

Scientific Computation - Apps

4 ./0

slide-13
SLIDE 13

13

Overview

4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

slide-14
SLIDE 14

14

Isosurfaces - Examples

Isolines Isosurfaces

slide-15
SLIDE 15

15

1

4

!

%

  • %
  • %

! 4

  • %

!

  • 2

3 4 5 4 3 6 7 8 3 4 6 9 6 4 2 4 8 8 4 : 2 2 4 3 ;<

slide-16
SLIDE 16

16

2"!

4

!!"

'=>( '?2=>( '=?2>( '=>?2( '=?2>?2( '?2=?2>?2( '?2=?2>( '?2=>?2(

slide-17
SLIDE 17

17

3"01

4

1 / '@( 'A;(

7

;6

7 7 < < 2: 2: 2:

;9 ; ;

slide-18
SLIDE 18

18

4"#$1

4

B!!1 1

2 3 8 4 5 6 7 <

;2 ;: 2222:2:: ::22:::: 1"

2 3 4 5 < 8 6 7

slide-19
SLIDE 19

19

5"C>C

4

.1

4 3<8!2<!

slide-20
SLIDE 20

20

5"1

4

1;:::::::2

4

2;!

  • !
slide-21
SLIDE 21

21

<"01

4

.1 1

;2: ;: ;7 ;<

  • ?2

1

[ ] [ ] [ ]

  • −

+ − + = i v i v i v T i x 1

slide-22
SLIDE 22

22

8"D

4

!1

1 , , 1 , , , 1 , , 1 , , , 1 , , 1 − + − + − +

− = − = − =

k j i k j i z k j i k j i y k j i k j i x

v v G v v G v v G

4 B

1

slide-23
SLIDE 23

23

6"E

slide-24
SLIDE 24

24

Overview

4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

slide-25
SLIDE 25

25

Direct Volume Rendering Examples

slide-26
SLIDE 26

26

'(

slide-27
SLIDE 27

27

  • 4

' (

4 ,

>

4

slide-28
SLIDE 28

28

.'.F(

4

'("

  • Human Tooth CT

α α α α(f)

RGB(f)

f

RGB

Shading, Compositing…

α α α α

Gordon Kindlmann

slide-29
SLIDE 29

29

.F

4

/

f α f α f α f α

Gordon Kindlmann

slide-30
SLIDE 30

30

.

4

#"

% >. % 1G1!H %

  • 4

$,

% !

Gordon Kindlmann

slide-31
SLIDE 31

31

'(

slide-32
SLIDE 32

32

C

4

B

  • C

!!

  • C;?!!?

C

!

  • s

s d d a a

I k I k I k I + + =

C;!??

slide-33
SLIDE 33

33

'(

slide-34
SLIDE 34

34

  • 4

I"

4 D"

3+ 2+

4 I" 4 D"

slide-35
SLIDE 35

35

  • 4

0J

4

1;@

4

  • %
  • %

! %

  • D

! C

slide-36
SLIDE 36

36

'(

slide-37
SLIDE 37

37

  • +
  • 1

$ $ .

slide-38
SLIDE 38

38

.

+

  • .

4

"1'E( !K F75

slide-39
SLIDE 39

39

$

+

  • $

4

"!L

slide-40
SLIDE 40

40

+

  • 1

4

"1= $

slide-41
SLIDE 41

41

$

+

  • $

4

">!E C M77

slide-42
SLIDE 42

42

color

  • pacity

!='(

1.0

slide-43
SLIDE 43

43

Overview

4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

slide-44
SLIDE 44

44

./0

4 % 1./0 4 D/% 4 $"

% / % +!=

slide-45
SLIDE 45

45

./1

slide-46
SLIDE 46

46

Techniques

  • I'/(

N.

slide-47
SLIDE 47

47

slide-48
SLIDE 48

48

  • !!

4

/

4 $////

slide-49
SLIDE 49

49

  • !

4 I/! 4 O!

slide-50
SLIDE 50

50

./0

4

slide-51
SLIDE 51

51

C'C(

4

1

PO

slide-52
SLIDE 52

52

Overview

4 What is SciVis? 4 Data & Applications 4 Iso-surfaces 4 Direct Volume Rendering 4 Vector Visualization 4 Challenges

slide-53
SLIDE 53

53

Challenges - Accuracy

4 Need metrics -> perceptual metric

(a) Original (b) Bias-Added (c) Edge-Distorted

slide-54
SLIDE 54

54

Challenges - Accuracy

4 Deal with unreliable data (noise, Ultrasound)

slide-55
SLIDE 55

55

Challenges - Accuracy

4 Irregular data sets

  • I"
  • !
  • BI"
slide-56
SLIDE 56

56

Challenges - Speed/Size

4 Efficient algorithms 4 Hardware developments (VolumePro) 4 Utilize current hardware (nVidia, ATI) 4 Compression schemes 4 Tera-byte data sets

slide-57
SLIDE 57

57

Challenges - HCI

4 Need better

interfaces

4 Which method

is best?

slide-58
SLIDE 58

58

Challenges - HCI

4 “Augmented” reality 4 Explore novel I/O devices