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Nonphotorealistic Visualization of Multidimensional Datasets SIGGRAPH 2001

Explore effective visualization techniques for large, multidimensional datasets with perceptual features and assisted navigation. Learn about cognitive vision, preattentive color selection, and perceptual texture elements experimentation to enhance dataset understanding.

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Nonphotorealistic Visualization of Multidimensional Datasets SIGGRAPH 2001

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  1. 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

  2. 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?

  3. Visualization Pipeline Multidimensional Dataset • Dataset Management • Visualization Assistant • Perceptual Visualization • Nonphotorealistic Visualization• Assisted Navigation Perception

  4. 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

  5. Temperature Windspeed Precipitation Pressure Separate Displays n = 42,224 elementsm = 4A1 = temperatureA2 = windspeedA3 = precipitationA4 = pressureV = colour F = dark blue  bright pink

  6. 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

  7. 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

  8. Preattentive Processing Video

  9. 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

  10. Colour Distance B A C CIE LUV isoluminant slice; AB = AC implies equal perceived colour difference

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

  12. Colour Category green A red T B blue purple Between named categories (T & B, harder) vs. within named categories (T & A, easier)

  13. Distance / Linear Separation Y d GY l R d B P Constant linear separation l, constant distance d to two nearest neighbours

  14. Example Experiment Displays 3 colours17 elements 7 colours49 elements Target: red square; 3-colour, 17 element displays and 7-colour, 49 element displays

  15. 3-Color w/LUV, Separation

  16. 7-Color w/LUV, Separation

  17. 7-Color w/LUV, Separation, Category

  18. CT Volume Visualization

  19. 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

  20. Pexel Examples Height Regularity Density

  21. Example “Taller” Display

  22. Example “Regular” Display

  23. Example “Regular” Display

  24. 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)

  25. 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

  26. 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

  27. 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?

  28. Water Lilies (The Clouds) 1903; Oil on canvas, 74.6 x 105.3 cm (29 3/8 x 41 7/16 in); Private collection

  29. 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

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

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

  32. 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

  33. 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

  34. 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

  35. 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

  36. Canyon Photo

  37. Canyon NPR

  38. Sloping Hills Photo

  39. Sloping Hills NPR

  40. 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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