1. 1 Visual Computing Perceptual Principles

2. 2 Visual Principles Vision as Knowledge Acquisition Pre-attentive Properties Gestalt Properties Sensory vs. Arbitrary Symbols Relative Expressiveness of Visual Cues

3. 3 Vision as Knowledge Acquisition Perception as a Constructive Act –What you see is not necessarily what you get Adaptation of vision to different lighting situations Image aftereffects Optical illusions Ambiguous figures

4. 4 Vision as Knowledge Acquisition Perception as Modeling the Environment –Evolutionary purpose –When you close your eyes, the world doesn’t disappear! –Examples: Visual completion Object occlusion Impossible objects

5. 5 Vision as Knowledge Acquisition Perception as Apprehension of Meaning –Classification –Attention and consciousness

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7. 7 Physical WorldVisual SystemMental Models Lights, surfaces, objects Eye, optic nerve, visual cortex Red, white, shape Stop sign STOP! StimulusPerceptionCognition External World

8. 8 Visual System Light path –Cornea, pupil, lens, retina –Optic nerve, brain Retinal cells –Rods and cones –Unevenly distributed Cones –Three “color receptors” –Concentrated in fovea Rods –Low-light receptor –Peripheral vision From Gray’s Anatomy

9. 9 Cone Response Encode spectra as three values –Long, medium and short (LMS) –Trichromacy: only LMS is “seen” –Different spectra can “look the same” Sort of like a digital camera* From A Field Guide to Digital Color, © A.K. Peters, 2003

10. 10 Eyes vs. Cameras Cameras –Good optics –Single focus, white balance, exposure –“Full image capture” Eyes –Relatively poor optics –Constantly scanning (saccades) –Constantly adjusting focus –Constantly adapting (white balance, exposure) –Mental reconstruction of image (sort of) http://www.usd.edu/psyc301/ChangeBlindness.htm

11. 11 Tracking Experiments

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13. 13

14. 14 Color is relative

15. 15 Interference RED GREEN BLUE PURPLE ORANGE Call out the color of the letters

16. 16 Interference PURPLE ORANGE GREEN BLUE RED Call out the color of the letters

17. 17 Preattentive Processing A limited set of visual properties are processed preattentively –(without need for focusing attention). This is important for design of visualizations –What can be perceived immediately? –Which properties are good discriminators? –What can mislead viewers?

18. 18 Example: Color Selection Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in color. From Healey 97 http://www.csc.ncsu.edu/faculty/healey/PP/index.html

19. 19 Example: Shape Selection Viewer can rapidly and accurately determine whether the target (red circle) is present or absent. Difference detected in form (curvature) From Healey 97 http://www.csc.ncsu.edu/faculty/healey/PP/index.html

20. 20 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 distractors, it is considered to be preattentive.

21. 21 Demonstration 13579345978274055 24937916478254137 23876597277103866 19874367259047362 95637283649105676 32543787954836754 56840378465485690 Time proportional to the number of digits 13579345978274055 24937916478254137 23876597277103866 19874367259047362 95637283649105676 32543787954836754 56840378465785690 Time proportional to the number of 7’s 13579345978274055 24937916478254137 23876597277103866 19874367259047362 95637283649105676 32543787954836754 56840378465785690 Both 3’s and 7’s seen preattentively Count the 7’s

22. 22 Contrast Creates Pop-out Hue and lightnessLightness only

23. 23 Pop-out vs. Distinguishable Pop-out –Typically, 5-6 distinct values simultaneously –Up to 9 under controlled conditions Distinguishable –20 easily for reasonable sized stimuli –More if in a controlled context –Usually need a legend

24. 24 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. From Healey 97 http://www.csc.ncsu.edu/faculty/healey/PP/index.html

25. 25 Example: Emergent Features Target has a unique feature with respect to distractors (open sides) and so the group can be detected preattentively.

26. 26 Example: Emergent Features Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively.

27. 27 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

28. 28 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

29. 29 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

30. 30 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]

31. 31 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

32. 32 Gestalt Properties Proximity Why perceive pairs vs. triplets?

33. 33 Gestalt Properties Similarity Slide adapted from Tamara Munzner

34. 34 Gestalt Properties Continuity Slide adapted from Tamara Munzner

35. 35 Gestalt Properties Connectedness Slide adapted from Tamara Munzner

36. 36 Gestalt Properties Closure Slide adapted from Tamara Munzner

37. 37 Gestalt Properties Symmetry Slide adapted from Tamara Munzner

38. 38 Gestalt Laws of Perceptual Organization (Kaufman 74) Figure and Ground –Escher illustrations are good examples –Vase/Face contrast Subjective Contour

39. 39 Unexpected Effects

40. 40 Emergence Holistic perception of image Slide adapted from Robert Kosara

41. 41 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

42. 42 Influence on Visualization Why we care –Exploit strengths, avoid weaknesses –Optimize, not interfere Design criteria –Effectiveness –Expressiveness –No false messages

43. 43 Design criteria: Effectiveness Faster to interpret More distinctions Fewer errors 01234567 This? Or this?

44. 44 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

45. 45 Which Properties are Appropriate for Which Information Types?

46. 46 Interpretations of Visual Properties Some properties can be discriminated more accurately but don’t have intrinsic meaning (Senay & Ingatious 97, Kosslyn, others) –Density (Greyscale) Darker -> More –Size / Length / Area Larger -> More –Position Leftmost -> first, Topmost -> first –Hue ??? no intrinsic meaning –Slope ??? no intrinsic meaning

47. 47 Rankings: Encoding quantitative data Cleveland & McGill 1984, adapted from Spence 2006

48. 48 Which properties used for what? Stephen Few’s Table: AttributeQuantitativeQualitative Line length X 2-D position X Orientation X Line width X Size X Shape X Curvature X Added marks X Enclosure X Hue X Intensity X