1. Intro to Human Visual System and Displays Fundamental Optics Fovea Perception These slides were developed by Colin Ware, Univ. of New Hampshire

2. Why Should We Be Interested In Visualization Hi bandwidth to the brain (70% of all receptors,40+% of cortex, 4 billion neurons) Can see much more than we can mentally image Can perceive patterns (what dimensionality?)

3. Perceptual versus Cultural

4. Basic Pathways

5. The machinery

6. Human Visual Field

7. Visual Angle

8. Acuities Vernier super acuity (10 sec) Grating acuity Two Point acuity (0.5 min)

9. Human Spatial Acuity

10. Cutoff at 50 cycles/deg. Receptors: 20 sec of arc Pooled over larger and larger areas 100 million receptors 1 million fibers to brain A screen may have 30 pixels/cm – need about 4 times as much. VR displays have 5 pixels/cm

11. Acuity Distribution

12. Brain Pixels

13. Brain pixels=retinal ganglion cell receptive fields Tartufieri Field size = 0.006(e+1.0) - Anderson Characters = 0.046e - Anstis Ganglion cells

14. Pixels and Brain Pixels 0.2 BP 1 bp Small Screen 0.8 BP Big Screen

15. Perception Many, many ways to trick the vision system.

22. Intro to Color for Information Display Color Theory Color Geometries Color applications Labeling Pseudo-color sequences

23. Trichromacy Three cones types in retina

24. Cone sensitivity functions

25. Color measurement Based on the “standard observer” CIE tristimulus values XYX Y is luminance. Assumes all humans are the same

26. Short wavelength sensitive cones Blue text on a dark background is to be avoided. We have very few short-wavelength sensitive cones in the retina and they are not very sensitive Blue text on dark background is to be avoided. We have very few short-wavelength sensitive cones in the retina and they are not very sensitive Blue text on a dark background is to be avoided. We have very few short-wavelength sensitive cones in the retina and they are not very sensitive. Chromatic aberration in the eye is also a problem Blue text on a dark background is to be avoided. We have very few short-wavelength sensitive cones in the retina and they are not very sensitive

27. Color Channels

28. Luminance “channel” Visual system extracts surface information Discounts illumination level Discounts color of illumination Mechanisms 1) Adaptation 2) Simultaneous contrast

29. Luminance is not Brightness Eye sensitive over 9 orders or magnitude 5 orders of magnitude (room – sunlight) Receptors bleach and become less sensitive with more light Takes up to half an hour to recover sensitivity We are not light meters

30. Luminance contrast

31. Contrast for constancy

33. Brightness Lightness and Luminance Brightness refers to perception of lights Brightness non linear Monitor Gamma Lightness refers to perception of surfaces Perceived lightness depends on a reference white

34. Luminance for Shape-from- shading

35. Channel Properties Luminance Channel Detail Form Shading Motion Stereo Chromatic Channels Surfaces of things Labels Berlin and Kay Categories (about 6- 10) Red, green, yellow and blue are special (unique hues)

36. Chromatic Channels have Low Spatial Resolution Luminance contrast needed to see detail 3:1 recommended 10:1 idea for small text

37. Color phenomena Small field tritanopiaChromatic contrast

38. Color “blindness” A 3D to a 2D space 8 % of males R-G color blindness Can generate color blind acceptable palette Yellow blue variation OK

39. Implications Color perception is relative We are sensitive to small differences- hence need sixteen million colors Not sensitive to absolute values- hence we can only use < 10 colors for coding

40. Color great for classification Rapid visual segmentation Color helps us determine type Only about six categories

41. Applications Color interfaces Color coding Color sequences Color for multi-dimensional discrete data

42. Color Coding Large areas: low saturation Small areas high saturation Break isoluminance with borders

43. Color Coding The same rules apply to color coding text and other similar information. Small areas should have high saturation colors, Large areas should be coded with low saturation colors Luminance contrast should be maintained

44. Visual Principles Sensory vs. Arbitrary Symbols Pre-attentive Properties Gestalt Properties Relative Expressiveness of Visual Cues

45. Sensory vs. Arbitrary Symbols Sensory: Understanding without training Resistance to instructional bias Sensory immediacy Hard-wired and fast Cross-cultural Validity Arbitrary Hard to learn Easy to forget Embedded in culture and applications

46. American Sign Language Primarily arbitrary, but partly representational Signs sometimes based partly on similarity But you couldn’t guess most of them They differ radically across languages Sublanguages in ASL are more representative Diectic terms Describing the layout of a room, there is a way to indicate by pointing on a plane where different items sit.

47. All Preattentive Processing figures from Healey 97 All Preattentive Processing figures from Healey 97 http://www.csc.ncsu.edu/faculty/healey/PP/PP.html Pre-attentive Processing A limited set of visual properties are processed pre-attentively (without need for focusing attention). This is important for design of visualizations What can be perceived immediately? What properties are good discriminators? What can mislead viewers?

48. Example: Color Selection Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in color.

49. Example: Shape Selection Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in form (curvature)

50. Pre-attentive Processing < 200 - 250ms qualifies as pre-attentive eye movements take at least 200ms yet certain processing can be done very quickly, implying low-level processing in parallel If a decision takes a fixed amount of time regardless of the number of distracters, it is considered to be pre-attentive.

51. Example: Conjunction of Features Viewer cannot rapidly and accurately determine whether the target (red circle) is present or absent when target has two or more features, each of which are present in the distractors. Viewer must search sequentially. All Preattentive Processing figures from Healey 97 All Preattentive Processing figures from Healey 97 http://www.csc.ncsu.edu/faculty/healey/PP/PP.html

52. Example: Emergent Features Target has a unique feature with respect to distractors (open sides) and so the group can be detected preattentively.

53. Example: Emergent Features Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.

54. Asymmetric and Graded Preattentive Properties Some properties are asymmetric a sloped line among vertical lines is preattentive a vertical line among sloped ones is not Some properties have a gradation some more easily discriminated among than others

55. Use Grouping of Well-Chosen Shapes for Displaying Multivariate Data

56. SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC

57. SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM GOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXO CERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEM SCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC Text NOT Preattentive

58. Preattentive Visual Properties (Healey 97) length Triesman & Gormican [1988] width Julesz [1985] size Triesman & Gelade [1980] curvature Triesman & Gormican [1988] number Julesz [1985]; Trick & Pylyshyn [1994] terminators Julesz & Bergen [1983] intersection Julesz & Bergen [1983] closure Enns [1986]; Triesman & Souther [1985] colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991] Kawai et al. [1995]; Bauer et al. [1996] intensity Beck et al. [1983]; Triesman & Gormican [1988] flicker Julesz [1971] direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992] binocular lustre Wolfe & Franzel [1988] stereoscopic depth Nakayama & Silverman [1986] 3-D depth cues Enns [1990] lighting direction Enns [1990]

59. Slide adapted from Tamara Munzner 59 Gestalt Principles Idea: forms or patterns transcend the stimuli used to create them. Why do patterns emerge? Under what circumstances? Principles of Pattern Recognition “gestalt” German for “pattern” or “form, configuration” Original proposed mechanisms turned out to be wrong Rules themselves are still useful

60. Gestalt Properties Proximity Why perceive pairs vs. triplets?

61. Slide adapted from Tamara Munzner 61 Gestalt Properties Similarity Slide adapted from Tamara Munzner

62. 62 Gestalt Properties Continuity Slide adapted from Tamara Munzner

63. 63 Gestalt Properties Connectedness Slide adapted from Tamara Munzner

64. 64 Gestalt Properties Closure Slide adapted from Tamara Munzner

65. 65 Gestalt Properties Symmetry Slide adapted from Tamara Munzner

66. Gestalt Laws of Perceptual Organization (Kaufman 74) Figure and Ground Escher illustrations are good examples Vase/Face contrast Subjective Contour

67. More Gestalt Laws Law of Common Fate like preattentive motion property move a subset of objects among similar ones and they will be perceived as a group

68. Pseudo-color sequences Issues: How can we see forms (quality) How we read value (quantity)

69. Pseudo-Color Sequences

70. Gray scale

71. Spectrum sequence

72. Color Sequences for Maps Color is poor for form and shape Color is naturally classified Luminance is good for form and shape Luminance results in contrast illusions A spiral sequence in color space - a good solution

73. Spiral Sequence

74. Luminance to signal direction

75. Take home messages Use luminance for detail, shape and form Use color for coding - few colors Minimize contrast effects Strong colors for small areas - contrast in luminance with background Subtle colors can be used to segment large areas