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Nonphotorealistic Visualization of Multidimensional Datasets SIGGRAPH 2001 Christopher G. Healey Department of Computer Science, North Carolina State University [email protected] http://www.csc.ncsu.edu/faculty/healey Supported by NSF-IIS-9988507, NSF-ACI-0083421

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Nonphotorealistic visualization of multidimensional datasets siggraph 2001 l.jpg

Nonphotorealistic Visualizationof Multidimensional DatasetsSIGGRAPH 2001

Christopher G. HealeyDepartment of Computer Science, North Carolina State [email protected]://www.csc.ncsu.edu/faculty/healeySupported by NSF-IIS-9988507, NSF-ACI-0083421


Goals of multidimensional visualization l.jpg
Goals of Multidimensional Visualization

  • Effective visualization of large, multidimensional datasets

  • size: number of elements nin dataset

  • dimensionality: number of attributes membedded in each element

  • Display effectively multiple attributes at a single spatial location?

  • Rapidly, accurately, and effortlessly explore large amounts of data?


Visualization pipeline l.jpg
Visualization Pipeline

Multidimensional Dataset

• Dataset Management

• Visualization Assistant

• Perceptual Visualization

• Nonphotorealistic Visualization• Assisted Navigation

Perception


Formal specification l.jpg
Formal Specification

  • Dataset D = { e1, …, en } containing n elements ei

  • D represents m data attributes A = { A1, …, Am }

  • Each ei encodes m attribute values ei = { ai,1, …, ai,m }

  • Visual features V = { V1, …, Vm } used to represent A

  • Function j: Aj Vj maps domain of Aj to range of displayable values in Vj

  • Data-feature mapping M( V, F ), a visual representation of D

  • Visualization: Selection of M and viewers interpretation of images produced by M


Separate displays l.jpg

Temperature

Windspeed

Precipitation

Pressure

Separate Displays

n = 42,224 elementsm = 4A1 = temperatureA2 = windspeedA3 = precipitationA4 = pressureV = colour

F = dark blue  bright pink


Integrated display l.jpg
Integrated Display

n = 42,224 elementsm = 4A1 = temperatureA2 = windspeedA3 = precipitationA4 = pressureV1 = colourV2 = sizeV3 = orientationV4 = density

F1 = dark blue  bright pink

F2 = 0.25  1.15

F3 = 0º  90º

F4 = 1x1  3x3


Cognitive vision l.jpg
Cognitive Vision

  • Psychological study of the human visual system

  • Perceptual (preattentive) features used to perform simple tasks in < 200 milliseconds

    • features: hue, intensity, orientation, size, length, curvature, closure, motion, depth of field, 3D cues

    • tasks:target detection, boundary detection, region tracking, counting and estimation

  • Perceptual (preattentive) tasks performed independent of display size

  • Develop, extend, and apply results to visualization



Effective hue selection l.jpg

A

B

A

B

C

D

E

F

Effective Hue Selection

  • How can we choose effectively multiple hues?

  • Suppose: { A, B } Suppose: { A, B, C, D, E, F }

  • Rapidly and accurately identifiable colors?

  • Equally distinguishable colors?

  • Maximum number of colors?

  • Three selection criteria: color distance, linear separation, color category


Colour distance l.jpg
Colour Distance

B

A

C

CIE LUV isoluminant slice; AB = AC implies equal perceived colour difference


Linear separation l.jpg
Linear Separation

A

C

T

B

Without linear separation (T in A & B, harder) vs. with linear separation (T in A & C, easier)


Colour category l.jpg
Colour Category

green

A

red

T

B

blue

purple

Between named categories (T & B, harder) vs. within named categories (T & A, easier)


Distance linear separation l.jpg
Distance / Linear Separation

Y

d

GY

l

R

d

B

P

Constant linear separation l, constant distance d to two nearest neighbours


Example experiment displays l.jpg
Example Experiment Displays

3 colours17 elements

7 colours49 elements

Target: red square; 3-colour, 17 element displays and 7-colour, 49 element displays






Perceptual texture elements l.jpg
Perceptual Texture Elements

  • Design perceptual texture elements (pexels)

  • Pexels support variation of perceptual texture dimensions height, density, regularity

  • Attach a pexel to each data element

  • Element attributes control pexel appearance

  • Psychophysical experiments used to measure:

    • perceptual salience of each texture dimension

    • visual interference between texture dimensions


Pexel examples l.jpg
Pexel Examples

Height

Regularity

Density


Example taller display l.jpg
Example “Taller” Display


Example regular display l.jpg
Example “Regular” Display


Example regular display23 l.jpg
Example “Regular” Display


Results l.jpg
Results

  • Subject accuracy used to measure performance

  • Taller pexels identified preattentively with no interference (93% accuracy)

  • Shorter, denser, sparser identified preattentively

  • Some height, density, regularity interference

  • Irregulardifficult to identify (76% accuracy); height, density interference

  • Regular cannot be identified (50% accuracy)


Typhoon visualization l.jpg
Typhoon Visualization

n = 572,474m = 3

A1 = windspeed;A2 = pressure;A3 = precipitation

V1 = height;V2 = density;V3 = color

f1 = short  tall;

f2 = dense  sparse;

f3 = blue  purple

Typhoon Amber approaches Taiwan, August 28, 1997


Typhoon visualization26 l.jpg
Typhoon Visualization

n = 572,474m = 3A1 = windspeed;A2 = pressure;A3 = precipitation

V1 = height;V2 = density;V3 = color

f1 = short  tall;

f2 = dense  sparse;

f3 = blue  purple

Typhoon Amber strikes Taiwan, August 29, 1997


Impressionism l.jpg
Impressionism

  • Underlying principles of impressionist art:

    • Object and environment interpenetrate

    • Colour acquires independence

    • Show a small section of nature

    • Minimize perspective

    • Solicit a viewer’s optics

  • Hue, luminance, color explicitly studied and controlled

  • Other stroke and style properties correspond closely to low-level visual features

    • path, length, energy, coarseness, weight

  • Can we bind data attributes with stroke properties?

  • Can we use perception to control painterly rendering?


Water lilies the clouds l.jpg
Water Lilies (The Clouds)

1903; Oil on canvas, 74.6 x 105.3 cm (29 3/8 x 41 7/16 in); Private collection


Rock arch west of etretat the manneport l.jpg
Rock Arch West of Etretat (The Manneport)

1883; Oil on canvas, 65.4 x 81.3 cm (25 3/4 x 32 in); Metropolitan Museum of Art, New York


Wheat field l.jpg
Wheat Field

1889; Oil on canvas, 73.5 x 92.5 cm (29 x 36 1/2 in); Narodni Galerie, Prague


Gray weather grande jatte l.jpg
Gray Weather, Grande Jatte

1888; Oil on canvas, 27 3/4 x 34 in; Philadelphia Museum of Art. Walter H. Annenberg Collection


Stroke feature correspondence l.jpg
StrokeFeature Correspondence

  • Close correspondence between Vj and Sj

    • hue  color, luminance  lighting, contrast  density, orientation  path, area  size

  • ei in D analogous to brush strokes in a painting

  • To build a painterly visualization of D:

    • construct M( V, F )

    • map Vj in V to corresponding painterly styles Sj in S

  • M now maps ei to brush strokes bi

  • ai,j in ei control painterly appearance of bi


Eastern us january l.jpg
Eastern US, January

n = 69,884m = 4

A1 = temperature;A2 = windspeed;A3 = pressure;A4 = precipitation

V1 = color;V2 = density;V3 = size;V4 = orientation

f1 = blue  pink;

f2 = sparse  dense;

f3 = small  large;f4 = upright  flat


Rocky mountains january l.jpg
Rocky Mountains, January

n = 69,884m = 4

A1 = temperature;A2 = windspeed;A3 = pressure;A4 = precipitation

V1 = color;V2 = density;V3 = size;V4 = orientation

f1 = blue  pink;

f2 = sparse  dense;

f3 = small  large;f4 = upright  flat


Pacific northwest february l.jpg
Pacific Northwest, February

n = 69,884m = 4

A1 = temperature;A2 = windspeed;A3 = pressure;A4 = precipitation

V1 = color;V2 = density;V3 = size;V4 = orientation

f1 = blue  pink;

f2 = sparse  dense;

f3 = small  large;f4 = upright  flat






Conclusions l.jpg
Conclusions

  • Formalisms identify a visual feature  painterly style correspondence

  • Can exploit correspondence to construct perceptually salient painterly visualizations

  • Recent and future work

    + psychophysical experiments confirm perceptual guidelines extend to painterly environment

    • subjective aesthetics experiments

    • improved computational models of painterly images

    • additional painterly styles

    • dynamic paintings (e.g., flicker, direction and velocity of motion)


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