1. 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

2. 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?

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

4. Formal Specification Dataset D = { e 1, …, e n } containing n elements e i D represents m data attributes A = { A 1, …, A m } Each e i encodes m attribute values e i = { a i,1, …, a i,m } Visual features V = { V 1, …, V m } used to represent A Function  j : A j  V j maps domain of A j to range of displayable values in V j Data-feature mapping M( V,  ), a visual representation of D Visualization: Selection of M and viewers interpretation of images produced by M

5. Separate Displays PrecipitationTemperatureWindspeedPressure n = 42,224 elements m = 4 A 1 = temperature A 2 = windspeed A 3 = precipitation A 4 = pressure V = colour  = dark blue  bright pink

6. Integrated Display n = 42,224 elements m = 4 A 1 = temperature A 2 = windspeed A 3 = precipitation A 4 = pressure V 1 = colour V 2 = size V 3 = orientation V 4 = density  1 = dark blue  bright pink  2 = 0.25  1.15  3 = 0º  90º  4 = 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. 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 ABABCDEF

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

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

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

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

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

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 Regularity Density Height

21. Example “ Taller ” Display

22. 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 Irregular difficult to identify (76% accuracy); height, density interference Regular cannot be identified (50% accuracy)

25. Typhoon Visualization n = 572,474 m = 3 A 1 = windspeed; A 2 = pressure; A 3 = precipitation V 1 = height; V 2 = density; V 3 = color  1 = short  tall;  2 = dense  sparse;  3 = blue  purple Typhoon Amber approaches Taiwan, August 28, 1997

26. Typhoon Visualization n = 572,474 m = 3 A 1 = windspeed; A 2 = pressure; A 3 = precipitation V 1 = height; V 2 = density; V 3 = color  1 = short  tall;  2 = dense  sparse;  3 = 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 V j and S j –hue  color, luminance  lighting, contrast  density, orientation  path, area  size e i in D analogous to brush strokes in a painting To build a painterly visualization of D: –construct M( V,  ) –map V j in V to corresponding painterly styles S j in S M now maps e i to brush strokes b i a i,j in e i control painterly appearance of b i

33. Eastern US, January n = 69,884 m = 4 A 1 = temperature; A 2 = windspeed; A 3 = pressure; A 4 = precipitation V 1 = color; V 2 = density; V 3 = size; V 4 = orientation  1 = blue  pink;  2 = sparse  dense;  3 = small  large;  4 = upright  flat

34. Rocky Mountains, January n = 69,884 m = 4 A 1 = temperature; A 2 = windspeed; A 3 = pressure; A 4 = precipitation V 1 = color; V 2 = density; V 3 = size; V 4 = orientation  1 = blue  pink;  2 = sparse  dense;  3 = small  large;  4 = upright  flat

35. Pacific Northwest, February n = 69,884 m = 4 A 1 = temperature; A 2 = windspeed; A 3 = pressure; A 4 = precipitation V 1 = color; V 2 = density; V 3 = size; V 4 = orientation  1 = blue  pink;  2 = sparse  dense;  3 = small  large;  4 = 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)