Nonphotorealistic visualization of multidimensional datasets siggraph 2001
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Nonphotorealistic Visualization of Multidimensional Datasets SIGGRAPH 2001 Christopher G. Healey Department of Computer Science, North Carolina State University healey@csc.ncsu.edu 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

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Nonphotorealistic Visualizationof Multidimensional DatasetsSIGGRAPH 2001

Christopher G. HealeyDepartment of Computer Science, North Carolina State Universityhealey@csc.ncsu.eduhttp://www.csc.ncsu.edu/faculty/healeySupported by NSF-IIS-9988507, NSF-ACI-0083421


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

Multidimensional Dataset

• Dataset Management

• Visualization Assistant

• Perceptual Visualization

• Nonphotorealistic Visualization• Assisted Navigation

Perception


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


Temperature

Windspeed

Precipitation

Pressure

Separate Displays

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

F = dark blue  bright pink


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

  • 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


Preattentive Processing Video


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

B

A

C

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


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

green

A

red

T

B

blue

purple

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


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

3 colours17 elements

7 colours49 elements

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


3-Color w/LUV, Separation


7-Color w/LUV, Separation


7-Color w/LUV, Separation, Category


CT Volume Visualization


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

Height

Regularity

Density


Example “Taller” Display


Example “Regular” Display


Example “Regular” Display


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

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 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

  • 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)

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)

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


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

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


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

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

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

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


Canyon Photo


Canyon NPR


Sloping Hills Photo


Sloping Hills NPR


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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