nonphotorealistic visualization of multidimensional datasets siggraph 2001 christopher g. healey...
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Nonphotorealistic Visualization
of Multidimensional DatasetsSIGGRAPH 2001
Christopher G. HealeyDepartment of Computer Science, North Carolina State
University
[email protected]://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)