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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

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
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
Visualization Pipeline

Multidimensional Dataset

• Dataset Management

• Visualization Assistant

• Perceptual Visualization

• Nonphotorealistic Visualization• Assisted Navigation

Perception

formal specification
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

Temperature

Windspeed

Precipitation

Pressure

Separate Displays

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

F = dark blue  bright pink

integrated display
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
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

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
Colour Distance

B

A

C

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

linear separation
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
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
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
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
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
Pexel Examples

Height

Regularity

Density

results
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
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
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
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
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
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
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
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
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
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
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
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
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)