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Visual Perception

Visual Perception . Cecilia R. Aragon I247 UC Berkeley Spring 2010. Acknowledgments. Thanks to slides and publications by Marti Hearst, Pat Hanrahan , Christopher Healey, Maneesh Agrawala , and Lawrence Anderson-Huang, Colin Ware, Daniel Carr. Visual perception. Thinking with our Eyes

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Visual Perception

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  1. Visual Perception Cecilia R. Aragon I247 UC Berkeley Spring 2010

  2. Acknowledgments • Thanks to slides and publications by Marti Hearst, Pat Hanrahan, Christopher Healey, ManeeshAgrawala, and Lawrence Anderson-Huang, Colin Ware, Daniel Carr. I 247

  3. Visual perception • Thinking with our Eyes • Structure of the Retina • Preattentive Processing • Detection • Estimating Magnitude • Change Blindness • Multiple Attributes • Gestalt I 247

  4. Thinking with our Eyes • 70% of body’s sense receptors reside in our eyes • Metaphors to describe understanding often refer to vision (“I see,” “insight,” “illumination”) • “The eye and the visual cortex of the brain form a massively parallel processor that provides the highest-bandwidth channel into human cognitive centers.” – Colin Ware, Information Visualization, 2004 • Important to understand how visual perception works in order to effectively design visualizations I 247

  5. Thinking with our Eyes • Working memory is extremely limited • How to overcome? • “The processing of grouping simple concepts into more complex ones is called chunking.” – Ware, 2004 • “The process of becoming an expert is largely one of learning to create effective chunks.” – Ware, 2004 I 247

  6. The Power of Visualization • “It is possible to have a far more complex concept structure represented externally in a visual display than can be held in visual and verbal working memories.” – Ware, 2004 I 247

  7. How the Eye Works The eye is not a camera! Attention is selective (filtering) Cognitive processes Psychophysics: concerned with establishing quantitative relations between physical stimulation and perceptual events. I 247

  8. How to Use Perceptual Properties • Information visualization should cause what is meaningful to stand out I 247

  9. The Optimal Display • Typical monitor: 35 pixels/cm • = 40 cycles per degree at normal viewing distances • Human eye: receptors packed into fovea at 180 per degree of visual angle • So a 4000x4000-pixel resolution monitor should be adequate for most visual perception tasks I 247

  10. Optimal spatial resolution • Humans can resolve a grating of 50 cycles per degree (~44 pixels per cm) • Sampling theory (Nyquist) states: need to sample at twice the highest frequency needed to detect • So… why is 150 pixels per degree not sufficient (cf. laser printers at 460 dots per cm)? • 3 reasons: aliasing, gray levels, superacuities • (will be discussed in future lecture) I 247

  11. Structure of the Retina I 247

  12. Structure of the Retina • The retina is not a camera! • Network of photo-receptorcells (rods and cones) andtheir connections [Anderson-Huang, L. http://www.physics.utoledo.edu/~lsa/_color/18_retina.htm] I 247

  13. Photo-transduction • When a photon enters a receptor cell (e.g. a rod or cone), it is absorbed by a molecule called 11-cis-retinalandconverted to trans form. • The different shapecauses it to ultimatelyreduce the electricalconductivity of thephoto-receptor cell. [Anderson-Huang, L. http://www.physics.utoledo.edu/~lsa/_color/18_retina.htm] I 247

  14. Retina • Photoreceptors: • 120 million rods, more sensitive than cones, not sensitive to color • 6-7 million cones, color sensitivity, concentrated in macula (central 12 degrees of visual field) • Fovea centralis - 2 degrees of visual field – twice the width of thumbnail at arm’s length) • Fovea comprises lessthan 1% of retinal sizebut 50% of visual cortex I 247

  15. Electric currents from photo-receptors • Photo-receptors generate an electrical current in the dark. • Light shuts off the current. • Each doubling of light causes roughly the same reduction of current (3 picoAmps for cones, 6 for rods). • Rods more sensitive, recover more slowly. • Cones recover faster, overshoot. • Geometrical response in scaling laws of perception. [Anderson-Huang, L. http://www.physics.utoledo.edu/~lsa/_color/18_retina.htm] I 247

  16. Preattentive Processing

  17. How many 5’s? 385720939823728196837293827 382912358383492730122894839 909020102032893759273091428 938309762965817431869241024 [Slide adapted from Joanna McGrenere http://www.cs.ubc.ca/~joanna/ ] I 247

  18. How many 5’s? 385720939823728196837293827 382912358383492730122894839 909020102032893759273091428 938309762965817431869241024 I 247

  19. Preattentive Processing • Certain basic visual properties are detected immediately by low-level visual system • “Pop-out” vs. serial search • Tasks that can be performed in less than 200 to 250 milliseconds on a complex display • Eye movements take at least 200 msec to initiate I 247

  20. Color (hue) is preattentive • Detection of red circle in group of blue circles is preattentive [image from Healey 2005] I 247

  21. Form (curvature) is preattentive • Curved form “pops out” of display [image from Healey 2005] I 247

  22. Conjunction of attributes • Conjunction target generally cannot be detected preattentively (red circle in sea of red square and blue circle distractors) [image from Healey 2005] I 247

  23. Healeyon preattentive processing http://www.csc.ncsu.edu/faculty/healey/PP/index.html I 247

  24. line orientation length width size curvature number terminators intersection Preattentive Visual Features closure color (hue) intensity flicker direction of motion stereoscopic depth 3D depth cues I 247

  25. Cockpit dials • Detection of a slanted line in a sea of vertical lines is preattentive I 247

  26. Detection I 247

  27. Just-Noticeable Difference • Which is brighter? I 247

  28. Just-Noticeable Difference • Which is brighter? (130, 130, 130) (140, 140, 140) I 247

  29. 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 ΔSto the initial stimulus Swas relatively constant: ΔS / S = k I 247

  30. 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. I 247

  31. Weber’s Law • Most continuous variations in magnitude are perceived as discrete steps • Examples: contour maps, font sizes I 247

  32. Estimating Magnitude I 247

  33. Stevens’ Power Law • Compare area of circles: I 247

  34. 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] I 247

  35. [Stevens 1961] Stevens’ Power Law I 247

  36. 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) I 247

  37. 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] I 247

  38. Relative Magnitude Estimation Most accurate Least accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value) I 247

  39. Change Blindness I 247

  40. Change Blindness • An interruption in what is being seen causes us to miss significant changes that occur in the scene during the interruption. • Demo from Ron Rensink: http://www.psych.ubc.ca/~rensink/flicker/ I 247

  41. Possible Causes of Change Blindness [Simons, D. J. (2000), Current approaches to change blindness, Visual Cognition, 7, 1-16. ] I 247

  42. Multiple Visual Attributes I 247

  43. The Game of Set • Color • Symbol • Number • Shading A set is 3 cards such that each feature is EITHER the same on each card OR is different on each card. [Set applet by AdrienTreuille, http://www.cs.washington.edu/homes/treuille/resc/set/] I 247

  44. Multiple Visual Attributes • Integral vs. separable • Integral dimensions • two or more attributes of an object are perceived holistically (e.g.width and height of rectangle). • Separable dimensions • judged separately, or through analytic processing (e.g. diameter and color of ball). • Separable dimensions are orthogonal. • For example, position is highly separable from color. In contrast, red and green hue perceptions tend to interfere with each other. I 247

  45. Integral vs. Separable Dimensions Integral Separable [Ware 2000] I 247

  46. Gestalt I 247

  47. Gestalt Principles • figure/ground • proximity • similarity • symmetry • connectedness • continuity • closure • common fate • transparency I 247

  48. Examples Figure/Ground Proximity Connectedness [from Ware 2004] [http://www.aber.ac.uk/media/Modules/MC10220/visper07.html] I 247

  49. Conclusion • What is currently known about visual perception can aid the design process. • Understanding low-level mechanisms of the visual processing system and using that knowledge can result in improved displays. I 247

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