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IAT 355 Introduction to Visual Analytics

IAT 355 Introduction to Visual Analytics. Perception. Perceptual Processing. Seek to better understand visual perception and visual information processing Multiple theories or models exist Need to understand physiology and cognitive psychology. A Simple Model. Two stage process

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IAT 355 Introduction to Visual Analytics

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  1. IAT 355 Introduction to Visual Analytics Perception IAT 355

  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 355

  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 355

  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 355

  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 355

  6. Stage 2 Attributes • Slow serial processing • Involves working and long-term memory • Top-down processing IAT 355

  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 355

  8. How Many 3’s? 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 IAT 355

  9. How Many 3’s? 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686 IAT 355

  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 355

  11. Examples: • Where is the red circle? Left or right? • Put your hand up as soon as you see it. IAT 355

  12. Pre-attentive Hue • Can be done rapidly IAT 355

  13. Examples: • Where is the red circle? Left or right? • Put your hand up as soon as you see it. IAT 355

  14. Shape IAT 355

  15. Examples: • Where is the red circle? Left or right? • Put your hand up as soon as you see it. IAT 355

  16. Hue and Shape • Cannot be done preattentively • Must perform a sequential search • Conjuction of features (shape and hue) causes it IAT 355

  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 355

  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 355

  19. Examples • Is there a boundary? IAT 355

  20. Hue versus Shape • Left: Boundary detected preattentively based on hue regardless of shape • Right: Cannot do mixed color shapes preattentively IAT 355

  21. Hue vs. Brightness • Left: Varying brightness seems to interfere • Right: Boundary based on brightness can be done preattentively IAT 355

  22. Preattentive Features • Certain visual forms lend themselves to preattentive processing • Variety of forms seem to work IAT 355

  23. 3-D Figures • 3-D visual reality has an influence IAT 355

  24. Emergent Features IAT 355

  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 355

  26. IAT 355 http://www.csc.ncsu.edu/faculty/healey/PP/

  27. IAT 355 http://www.csc.ncsu.edu/faculty/healey/PP/

  28. Key Perceptual Properties • Brightness • Color • Texture • Shape IAT 355

  29. Luminance/Brightness • Luminance • Measured amount of light coming from some place • Brightness • Perceived amount of light coming from source IAT 355

  30. 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 355

  31. 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 355

  32. Greyscale • White and Black are not fixed IAT 355

  33. Greyscale • White and Black are not fixed! IAT 355

  34. 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 355

  35. Color Systems • HSV: Hue, Saturation, Value • Hue: Color type • Saturation: “Purity” of color • Value: Brightness IAT 355

  36. CIE Space • The perceivable set of colors IAT 355

  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 355

  38. Why 12 colors? IAT 355

  39. Just-Noticeable Difference • Which is brighter? IAT 355

  40. Just-Noticeable Difference • Which is brighter? (130, 130, 130) (140, 140, 140) IAT 355

  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 355

  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 355

  43. Weber’s Law • Most continuous variations in magnitude are perceived as discrete steps • Examples: contour maps, font sizes IAT 355

  44. Stevens’ Power Law • Compare area of circles: 1 20 IAT 355

  45. 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 355

  46. [Stevens 1961] Stevens’ Power Law IAT 355

  47. 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 355

  48. 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 355

  49. Relative Magnitude Estimation Most accurate Least accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value) IAT 355

  50. Color Sequence • Can you order these? IAT 355

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