Nonphotorealistic Visualization

of Multidimensional DatasetsSIGGRAPH 2001

Christopher G. HealeyDepartment of Computer Science, North Carolina State

University

healey@csc.ncsu.eduhttp://www.csc.ncsu.edu/faculty/healey

Supported by NSF-IIS-9988507, NSF-ACI-0083421

Goals of Multidimensional Visualization• Effective visualization of large, multidimensional

datasets

• size: number of elements n in dataset

• dimensionality: number of attributes m embedded in each element

• Display effectively multiple attributes at a single spatial location?

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

Visualization Pipeline

• Dataset Management • Visualization Assistant• Perceptual Visualization• Nonphotorealistic Visualization• Assisted Navigation

Multidimensional Dataset

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, ), a visual representation of D

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

Separate Displays

Precipitation

Temperature Windspeed

Pressure

n = 42,224 elementsm = 4

A1 = temperatureA2 = windspeedA3 = precipitationA4 = pressure

V = colour

= dark blue bright pink

Integrated Display

n = 42,224 elementsm = 4

A1 = temperatureA2 = windspeedA3 = precipitationA4 = pressure

V1 = colourV2 = sizeV3 = orientationV4 = density

1 = dark blue bright pink2 = 0.25 1.153 = 0º 90º4 = 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

• 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

Effective Hue Selection

A B A B C D E F

Colour Distance

A

B

C

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

Linear Separation

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

A

B

T

C

Colour Category

red

purpleblue

green

B

A

T

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

Distance / Linear Separation

B

GY

Y

R

P

l

d

d

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

Example Experiment Displays

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

3 colours17 elements

7 colours49 elements

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

Regularity Density Height

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

• Irregular difficult 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

1 = short tall;2 = dense sparse;3 = blue purple

Typhoon Amber approaches Taiwan, August 28, 1997

Typhoon Visualization

n = 572,474m = 3

A1 = windspeed;A2 = pressure;A3 = precipitation

V1 = height;V2 = density;V3 = color

1 = short tall;2 = dense sparse;3 = 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

StrokeFeature 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, )– 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

1 = blue pink;2 = sparse dense;3 = small large;4 = 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

1 = blue pink;2 = sparse dense;3 = small large;4 = 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

1 = blue pink;2 = sparse dense;3 = small large;4 = 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)