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

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Presentation on theme: "Visual Perception Cecilia R. Aragon I247 UC Berkeley Spring 2010."— Presentation transcript:

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, Maneesh Agrawala, and Lawrence Anderson- Huang, Colin Ware, Daniel Carr. Spring 2010I 2472

3 Visual perception Thinking with our Eyes Structure of the Retina Preattentive Processing Detection Estimating Magnitude Change Blindness Multiple Attributes Gestalt Spring 2010I 2473

4 Thinking with our Eyes 70% of bodys 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 Spring 2010I 2474

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 Spring 2010I 2475

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 Spring 2010I 2476

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. Spring 2010I 2477

8 How to Use Perceptual Properties Information visualization should cause what is meaningful to stand out Spring 2010I 2478

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 Spring 2010I 2479

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) Spring 2010I 24710

11 Structure of the Retina Spring 2010I 24711

12 Structure of the Retina The retina is not a camera! Network of photo-receptor cells (rods and cones) and their connections Spring 2010I 24712 [Anderson-Huang, L. _color/18_retina.htm]

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-retinal and converted to trans form. The different shape causes it to ultimately reduce the electrical conductivity of the photo-receptor cell. Spring 2010I 24713 [Anderson-Huang, L.]

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 arms length) – Fovea comprises less than 1% of retinal size but 50% of visual cortex Spring 2010I 24714

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. Spring 2010I 24715 [Anderson-Huang, L.]

16 Preattentive Processing

17 How many 5s? 385720939823728196837293827 382912358383492730122894839 909020102032893759273091428 938309762965817431869241024 Spring 2010I 24717 [Slide adapted from Joanna McGrenere ]

18 How many 5s? 385720939823728196837293827 382912358383492730122894839 909020102032893759273091428 938309762965817431869241024 Spring 2010I 24718

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 Spring 2010I 24719

20 Color (hue) is preattentive Detection of red circle in group of blue circles is preattentive Spring 2010I 24720 [image from Healey 2005]

21 Form (curvature) is preattentive Curved form pops out of display Spring 2010I 24721 [image from Healey 2005]

22 Conjunction of attributes Conjunction target generally cannot be detected preattentively (red circle in sea of red square and blue circle distractors) Spring 2010I 24722 [image from Healey 2005]

23 Healey on preattentive processing Spring 2010I 24723

24 Preattentive Visual Features Spring 2010 I 24724 line orientation length width size curvature number terminators intersection closure color (hue) intensity flicker direction of motion stereoscopic depth 3D depth cues

25 Cockpit dials Detection of a slanted line in a sea of vertical lines is preattentive Spring 2010I 24725

26 Detection Spring 2010I 24726

27 Just-Noticeable Difference Which is brighter? Spring 2010I 24727

28 Just-Noticeable Difference Which is brighter? Spring 2010I 24728 (130, 130, 130)(140, 140, 140)

29 Webers Law In the 1830s, 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 Spring 2010I 24729

30 Webers 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. Spring 2010I 24730

31 Webers Law Most continuous variations in magnitude are perceived as discrete steps Examples: contour maps, font sizes Spring 2010I 24731

32 Estimating Magnitude Spring 2010I 24732

33 Stevens Power Law Compare area of circles: Spring 2010I 24733

34 Stevens Power Law s(x) = ax b 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 Spring 2010 I 24734 [graph from Wilkinson 99]

35 Stevens Power Law Spring 2010 I 24735 [Stevens 1961]

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) Spring 2010I 24736

37 Stevens Power Law Apparent magnitude scaling Spring 2010I 24737 [Cartography: Thematic Map Design, p. 170, Dent, 96] S = 0.98A 0.87 [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]

38 Relative Magnitude Estimation Most accurate Least accurate Position (common) scale Position (non-aligned) scale Length Slope Angle Area Volume Color (hue/saturation/value) Spring 2010I 24738

39 Change Blindness Spring 2010I 24739

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: Spring 2010I 24740

41 Possible Causes of Change Blindness Spring 2010I 24741 [Simons, D. J. (2000), Current approaches to change blindness, Visual Cognition, 7, 1-16. ]

42 Multiple Visual Attributes Spring 2010I 24742

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. Spring 2010I 24743 [Set applet by Adrien Treuille, http://www.cs.]

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. Spring 2010I 24744

45 Integral vs. Separable Dimensions Integral Separable Spring 2010I 24745 [Ware 2000]

46 Gestalt Spring 2010I 24746

47 Gestalt Principles figure/ground proximity similarity symmetry connectedness continuity closure common fate transparency Spring 2010I 24747

48 Examples Figure/Ground Spring 2010I 24748 [] Proximity Connectedness [from Ware 2004]

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. Spring 2010I 24749

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