IAT 814 Introduction to Visual Analytics

1. IAT 814 Introduction to Visual Analytics Perception IAT 814

2. Perceptual Processing • Seek to better understand visual perception and visual information processing • Multiple theories or models exist • Need to understand physiology and cognitive psychology IAT 814

3. A Simple Model • Two stage process • Parallel extraction of low-level properties of scene • Sequential goal-directed processing Stage 1 Early, parallel detection of color, texture, shape, spatial attributes Stage 2 Serial processing of object identification (using memory) and spatial layout, action Eye IAT 814

4. Stage 1 - Low-level, Parallel • Neurons in eye & brain responsible for different kinds of information • Orientation, color, texture, movement, etc. • Arrays of neurons work in parallel • Occurs “automatically” • Rapid • Information is transitory, briefly held in iconic store • Bottom-up data-driven model of processing • Often called “pre-attentive” processing IAT 814

5. Stage 2 - Sequential, Goal-Directed • Splits into subsystems for object recognition and for interacting with environment • Increasing evidence supports independence of systems for symbolic object manipulation and for locomotion & action • First subsystem then interfaces to verbal linguistic portion of brain, second interfaces to motor systems that control muscle movements IAT 814

6. Stage 2 Attributes • Slow serial processing • Involves working and long-term memory • Top-down processing IAT 814

7. Preattentive Processing • How does human visual system analyze images? • Some things seem to be done preattentively, without the need for focused attention • Generally less than 200-250 msecs (eye movements take 200 msecs) • Seems to be done in parallel by low-level vision system IAT 814

8. How Many 3’s? 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 IAT 814

9. How Many 3’s? 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 IAT 814

10. What Kinds of Tasks? • Target detection • Is something there? • Boundary detection • Can the elements be grouped? • Counting • How many elements of a certain type are present? IAT 814

11. Examples: • Where is the red circle? Left or right? • Put your hand up as soon as you see it. IAT 814

12. Pre-attentive Hue • Can be done rapidly IAT 814

13. Examples: • Where is the red circle? Left or right? • Put your hand up as soon as you see it. IAT 814

14. Shape IAT 814

15. Examples: • Where is the red circle? Left or right? • Put your hand up as soon as you see it. IAT 814

16. Hue and Shape • Cannot be done preattentively • Must perform a sequential search • Conjuction of features (shape and hue) causes it IAT 814

17. Examples • Is there a boundary: • A connected chain of features that cross the rectangle • Put your hand up as soon as you see it. IAT 814

18. Fill and Shape • Left can be done preattentively since each group contains one unique feature • Right cannot (there is a boundary!) since the two features are mixed (fill and shape) IAT 814

19. Examples • Is there a boundary? IAT 814

20. Hue versus Shape • Left: Boundary detected preattentively based on hue regardless of shape • Right: Cannot do mixed color shapes preattentively IAT 814

21. Hue vs. Brightness • Left: Varying brightness seems to interfere • Right: Boundary based on brightness can be done preattentively IAT 814

22. Preattentive Features • Certain visual forms lend themselves to preattentive processing • Variety of forms seem to work • In the next slide, spot the region of different shapes, both left and right IAT 814

23. 3-D Figures • 3-D visual reality has an influence IAT 814

24. Emergent Features IAT 814

25. Potential PA Features • length • width • size • curvature • number • terminators • intersection • closure • hue • intensity • flicker • direction of motion • stereoscopic depth • 3-D depth cues • lighting direction IAT 814

26. Key Perceptual Properties • Brightness • Color • Texture • Shape IAT 814

27. Luminance/Brightness • Luminance • Measured amount of light coming from some place • Brightness • Perceived amount of light coming from source IAT 814

28. Brightness • Perceived brightness is non-linear function of amount of light emitted by source Typically a power function S = aIn S - sensation I - intensity • Very different on screen versus paper IAT 814

29. Greyscale • Probably not best way to encode data because of contrast issues • Surface orientation and surroundings matter a great deal • Luminance channel of visual system is so fundamental to so much of perception • We can get by without color discrimination, but not luminance IAT 814

30. Greyscale • White and Black are not fixed IAT 814

31. Greyscale • White and Black are not fixed! IAT 814

32. Color Systems • HSV: Hue, Saturation, Value • Hue: Color type • Saturation: “Purity” of color • Value: Brightness IAT 814

33. CIE Space • The perceivable set of colors IAT 814

34. CIE L*a*b* • http://www.gamutvision.com/docs/printest.html IAT 814

35. Color Categories • Are there certain canonical colors? • Post & Greene ’86 had people name different colors on a monitor • Pictured are ones with > 75% commonality IAT 814

36. Luminance • Foreground must be distinct from background! Can you read this text? Can you read this text? Can you read this text? Can you read this text? Can you read this text? Can you read this text? IAT 814

37. Color for Categories • Can different colors be used for categorical variables? • Yes (with care) • Colin Ware’s suggestion: 12 colors • red, green, yellow, blue, black, white, pink, cyan, gray, orange, brown, purple IAT 814

38. Why 12 colors? IAT 814

39. Just-Noticeable Difference • Which is brighter? IAT 814

40. Just-Noticeable Difference • Which is brighter? (130, 130, 130) (140, 140, 140) IAT 814

41. Weber’s Law • In the 1830’s, Weber made measurements of the just-noticeable differences (JNDs) in the perception of weight and other sensations. • He found that for a range of stimuli, the ratio of the JND ΔS to the initial stimulus S was relatively constant: ΔS / S = k IAT 814

42. Weber’s Law • Ratios more important than magnitude in stimulus detection • For example: we detect the presence of a change from 100 cm to 101 cm with the same probability as we detect the presence of a change from 1 to 1.01 cm, even though the discrepancy is 1 cm in the first case and only .01 cm in the second. IAT 814

43. Weber’s Law • Most continuous variations in magnitude are perceived as discrete steps • Examples: contour maps, font sizes IAT 814

44. Weber’s Law • Most continuous variations in magnitude are perceived as discrete steps • Examples: contour maps, font sizes IAT 814

45. Stevens’ Power Law • Compare area of circles: IAT 814

46. Stevens’ Power Law s(x) = axb s is the sensation x is the intensity of the attribute a is a multiplicative constant b is the power b > 1: overestimate b < 1: underestimate [graph from Wilkinson 99] IAT 814

47. [Stevens 1961] Stevens’ Power Law IAT 814

48. Stevens’ Power Law Experimental results for b: Length .9 to 1.1 Area .6 to .9 Volume .5 to .8 Heuristic: b ~ 1/sqrt(dimensionality) IAT 814

49. Stevens’ Power Law • Apparent magnitude scaling [Cartography: Thematic Map Design, p. 170, Dent, 96] S = 0.98A0.87 [J. J. Flannery, The relative effectiveness of some graduated point symbols in the presentation of quantitative data, Canadian Geographer, 8(2), pp. 96-109, 1971] [slide from Pat Hanrahan] IAT 814

50. Relative Magnitude Estimation Most accurate Least accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value) IAT 814