Visual Computing - Seidenberg School of Computer Science and ...

Visual Computing - Seidenberg School of Computer Science and ...

Visual Computing Perceptual Principles 1 Visual Principles

Vision as Knowledge Acquisition Pre-attentive Properties Gestalt Properties Sensory vs. Arbitrary Symbols Relative Expressiveness of Visual Cues 2 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 3 Vision as Knowledge Acquisition Perception as Modeling the Environment Evolutionary purpose When you close your eyes, the world doesnt disappear! Examples:

Visual completion Object occlusion Impossible objects 4 Vision as Knowledge Acquisition Perception as Apprehension of Meaning Classification Attention and consciousness 5

6 Physical World Visual System Mental Models Lights, surfaces, objects Eye, optic nerve, visual

cortex Red, white, shape Stop sign STOP! External World Stimulus Perception Cognition

7 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 8 From Grays Anatomy 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 9 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 10 Tracking Experiments 11 12 13

Color is relative 14 Interference RED

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

PURPLE ORANGE GREEN BLUE RED Call out the color of the letters 16 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? 17 Example: Color Selection Viewer can rapidly and accurately determine whether the target (red circle) is present or

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

19 From Healey 97 http://www.csc.ncsu.edu/faculty/healey/PP/index.html 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. 20 Demonstration Count the 7s 13579345978274055 24937916478254137 23876597277103866 19874367259047362 95637283649105676 32543787954836754 56840378465485690

13579345978274055 24937916478254137 23876597277103866 19874367259047362 95637283649105676 32543787954836754 56840378465785690 13579345978274055 24937916478254137 23876597277103866 19874367259047362

95637283649105676 32543787954836754 56840378465785690 Time proportional to the number of digits Time proportional to the number of 7s Both 3s and 7s seen preattentively

21 Contrast Creates Pop-out Hue and lightness Lightness only 22 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 23

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

Target has a unique feature with respect to distractors (open sides) and so the group can be detected preattentively. 25 Example: Emergent Features Target does not have a unique feature with respect to distractors and so the group cannot be detected preattentively. 26

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 27 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 28 Text NOT Preattentive

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

Preattentive Visual Properties (Healey 97) length width size curvature number terminators intersection closure colour (hue) intensity flicker

direction of motion binocular lustre stereoscopic depth 3-D depth cues lighting direction Triesman & Gormican [1988] Julesz [1985] Triesman & Gelade [1980] Triesman & Gormican [1988] Julesz [1985]; Trick & Pylyshyn [1994] Julesz & Bergen [1983] Julesz & Bergen [1983]

Enns [1986]; Triesman & Souther [1985] Nagy & Sanchez [1990, 1992]; D'Zmura [1991] Kawai et al. [1995]; Bauer et al. [1996] Beck et al. [1983]; Triesman & Gormican [1988] Julesz [1971] Nakayama & Silverman [1986]; Driver & McLeod [1992] Wolfe & Franzel [1988] Nakayama & Silverman [1986] Enns [1990] Enns [1990] 30

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 31 Gestalt Properties

Proximity Why perceive pairs vs. triplets? 32 Gestalt Properties Similarity 33 Slide adapted from Tamara Munzner Gestalt Properties Continuity

34 Slide adapted from Tamara Munzner Gestalt Properties Connectedness 35 Slide adapted from Tamara Munzner Gestalt Properties Closure

36 Slide adapted from Tamara Munzner Gestalt Properties Symmetry 37 Slide adapted from Tamara Munzner Gestalt Laws of Perceptual Organization (Kaufman 74) Figure and Ground Escher illustrations are good

examples Vase/Face contrast Subjective Contour 38 Unexpected Effects 39 Emergence Holistic perception of

image 40 Slide adapted from Robert Kosara 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 41

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

Faster to interpret More distinctions Fewer errors This? Or this? 0 1 2

3 4 5 43 6 7 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 44

Which Properties are Appropriate for Which Information Types? 45 Interpretations of Visual Properties Some properties can be discriminated more accurately but dont 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 46

Rankings: Encoding quantitative data 47 from Spence 2006 Cleveland & McGill 1984, adapted Which properties used for what? Attribute Stephen Fews Table: Quantitative

Line length X 2-D position X Qualitative Orientation

X Line width X Size X Shape X

Curvature X Added marks X Enclosure X

Hue X Intensity X 48

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