Marti Hearst SIMS 247 SIMS 247 Lecture 12 Visual Properties and Visualization February 26, 1998.

Marti HearstSIMS 247

SIMS 247 Lecture 12SIMS 247 Lecture 12Visual Properties and VisualizationVisual Properties and Visualization

February 26, 1998February 26, 1998


TodayToday

• Preattentive ProcessingPreattentive Processing

• Accuracy of Interpretation of Visual Accuracy of Interpretation of Visual PropertiesProperties

• Illusions and the Relation to Illusions and the Relation to Graphical IntegrityGraphical Integrity


Preattentive ProcessingPreattentive Processing

• A limited set of visual properties are A limited set of visual properties are processed preattentively (without need for processed preattentively (without need for focusing attention).focusing attention).

• This is important for design of This is important for design of visualizationsvisualizations– what can be perceived immediately– what properties are good discriminators– what can mislead viewers

All Preattentive Processing figures from Healey 97 (on the web)All Preattentive Processing figures from Healey 97 (on the web)


Example: Color SelectionExample: Color Selection

Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in color.


Example: Shape SelectionExample: Shape Selection

Viewer can rapidly and accurately determinewhether the target (red circle) is present or absent.Difference detected in form (curvature)


Pre-attentive ProcessingPre-attentive Processing

• < 200 - 250ms qualifies as pre-attentive< 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 If a decision takes a fixed amount of time regardless of the number of time regardless of the number of distractors, it is considered to be distractors, it is considered to be preattentive.preattentive.


Example: Conjunction of FeaturesExample: Conjunction of Features

Viewer cannotcannot rapidly and accurately determinewhether the target (red circle) is present or absent when target has two or more features, each of which arepresent in the distractors. Viewer must search sequentially.


Example: Conjunction of FeaturesExample: Conjunction of Features

Viewer cannotcannot rapidly and accurately determinethe boundary if it is determined by features that areshared across groups. On the right the boundary is determined by a conjunction of shape and value and cannot be detected preattentively; the lefthand boundary can


Example: Form vs. HueExample: Form vs. Hue

Hue based boundary determined preattentively regardlessof variation in form (left). However, the converse is not true (right).


Example: Hue vs. BrightnessExample: Hue vs. Brightness

Random intensity of brightness interferes with boundary detection (left). Uniform intensity allowsfor preattentive boundary recognition (right).


More on Conjunctive SearchesMore on Conjunctive Searches

• However, some conjunctive However, some conjunctive searches are preattentivesearches are preattentive– some involving motion, color, depth

work– other exceptions to the kinds of cases

shown here can be found


Example: Emergent FeaturesExample: Emergent Features

Target has a unique feature with respect to distractors (open sides) and so the groupcan be detected preattentively.


Example: Emergent FeaturesExample: Emergent Features

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


Asymmetric and Graded Asymmetric and Graded Preattentive PropertiesPreattentive Properties

• Some properties are asymmetricSome properties are asymmetric– a sloped line among vertical lines is

preattentive– a vertical line among sloped ones is not

• Some properties have a gradationSome properties have a gradation– some more easily discriminated among

than others


Use Grouping of Well-Chosen Use Grouping of Well-Chosen Shapes for Displaying Multivariate DataShapes for Displaying Multivariate Data


NOT Preattentive: Meaning NOT Preattentive: Meaning Represented by TextRepresented by Text

SUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMGOVERNS PRECISE EXAMPLE MERCURY SNREVOG ESICERP ELPMAXE YRUCREMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOCSUBJECT PUNCHED QUICKLY OXIDIZED TCEJBUS DEHCNUP YLKCIUQ DEZIDIXOCERTAIN QUICKLY PUNCHED METHODS NIATREC YLKCIUQ DEHCNUP SDOHTEMSCIENCE ENGLISH RECORDS COLUMNS ECNEICS HSILGNE SDROCER SNMULOC


Preattentive Visual PropertiesPreattentive Visual Properties(Healey 97)(Healey 97)

• length Triesman & Gormican [1988]length Triesman & Gormican [1988]• width Julesz [1985]width Julesz [1985]• size Triesman & Gelade [1980]size Triesman & Gelade [1980]• curvature Triesman & Gormican [1988]curvature Triesman & Gormican [1988]• number Julesz [1985]; Trick & Pylyshyn [1994]number Julesz [1985]; Trick & Pylyshyn [1994]• terminators Julesz & Bergen [1983]terminators Julesz & Bergen [1983]• intersection Julesz & Bergen [1983]intersection Julesz & Bergen [1983]• closure Enns [1986]; Triesman & Souther [1985]closure Enns [1986]; Triesman & Souther [1985]• colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991] colour (hue) Nagy & Sanchez [1990, 1992]; D'Zmura [1991]

Kawai et al. [1995]; Bauer et al. [1996] Kawai et al. [1995]; Bauer et al. [1996]• intensity Beck et al. [1983]; Triesman & Gormican [1988]intensity Beck et al. [1983]; Triesman & Gormican [1988]• flicker Julesz [1971]flicker Julesz [1971]• direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992]direction of motion Nakayama & Silverman [1986]; Driver & McLeod [1992]• binocular lustre Wolfe & Franzel [1988]binocular lustre Wolfe & Franzel [1988]• stereoscopic depth Nakayama & Silverman [1986]stereoscopic depth Nakayama & Silverman [1986]• 3-D depth cues Enns [1990]3-D depth cues Enns [1990]• lighting direction Enns [1990]lighting direction Enns [1990]


Gestalt PropertiesGestalt Properties

• Gestalt:Gestalt: form or configuration form or configuration• Idea: forms or patterns transcend Idea: forms or patterns transcend

the stimuli used to create them.the stimuli used to create them.– Why do patterns emerge?– Under what circumstances?

Why perceive pairs vs. triplets?


Gestalt Laws of Perceptual Gestalt Laws of Perceptual Organization Organization (Kaufman 74)(Kaufman 74)

• Law of Proximity Law of Proximity – Stimulus elements that are close together

will be perceived as a group

• Law of SimilarityLaw of Similarity– like the preattentive processing examples

• Law of Common FateLaw of Common Fate– like preattentive motion property

• move a subset of objects among similar ones and they will be perceived as a group


More Gestalt LawsMore Gestalt Laws

• Figure and GroundFigure and Ground– Escher illustrations are good

examples– Vase/Face contrast

• Subjective ContourSubjective Contour


M.C. Escher “Heaven and Hell”M.C. Escher “Heaven and Hell”


Which Properties for What Which Properties for What Information Types?Information Types?

• We’ve looked at preattentive We’ve looked at preattentive processesprocesses– how quickly can individuals be selected

• Also at wholistic, grouping effectsAlso at wholistic, grouping effects• Still have to consider what kind of Still have to consider what kind of

properties are effective for displaying properties are effective for displaying different kinds of informationdifferent kinds of information


Accuracy Ranking of Quantitative Perceptual TasksAccuracy Ranking of Quantitative Perceptual Tasks(Mackinlay 88 from Cleveland & McGill)(Mackinlay 88 from Cleveland & McGill)

Position

Length

Angle Slope

Area

Volume

Color Density

More Accurate

Less Accurate


Ranking of Applicability of Properties Ranking of Applicability of Properties for Different Data Typesfor Different Data Types

(Mackinlay 86, Not Empirically Verified)(Mackinlay 86, Not Empirically Verified)

QUANTITATIVE ORDINAL NOMINAL

Position Position PositionLength Density Color HueAngle Color Saturation TextureSlope Color Hue ConnectionArea Texture ContainmentVolume Connection DensityDensity Containment Color SaturationColor Saturation Length ShapeColor Hue Angle Length


QUANTITATIVE ORDINAL NOMINAL

Position Position PositionLength Density Color HueAngle Color Saturation TextureSlope Color Hue ConnectionArea Texture ContainmentVolume Connection DensityDensity Containment Color SaturationColor Saturation Length ShapeColor Hue Angle Length

Ranking of Applicability of Properties Ranking of Applicability of Properties for Different Data Typesfor Different Data Types

(Mackinlay 86, Not Empirically Verified)(Mackinlay 86, Not Empirically Verified)


Interpretations of Visual PropertiesInterpretations of Visual PropertiesSome properties can be discriminated more Some properties can be discriminated more accurately but don’t have intrinsic meaningaccurately but don’t have intrinsic meaning(Senay & Ingatious 97, Kosslyn, others)(Senay & Ingatious 97, Kosslyn, others)

– Density (Greyscale)Darker -> More

– Size / Length / AreaLarger -> More

– PositionLeftmost -> first, Topmost -> first

– Hue??? no intrinsic meaning

– Slope??? no intrinsic meaning


Example: Putting It TogetherExample: Putting It Together(Healey 98)(Healey 98)

Height: level of cultivationGreyscale: vegetation typeDensity: ground type


Visual IllusionsVisual Illusions

• People don’t perceive length, area, angle, People don’t perceive length, area, angle, brightness they way they “should”.brightness they way they “should”.

• Some illusions have been reclassified as Some illusions have been reclassified as systematic perceptual errorssystematic perceptual errors– brightness contrasts (grey square on white

background vs. on black background)– partly due to increase in our understanding of

the relevant parts of the visual system

• Nevertheless, the visual system does Nevertheless, the visual system does some really unexpected things.some really unexpected things.


Illusions of Linear ExtentIllusions of Linear Extent

• Mueller-Lyon (off by 25-30%)Mueller-Lyon (off by 25-30%)

• Horizontal-VerticalHorizontal-Vertical


Illusions of AreaIllusions of Area

• Delboeuf IllusionDelboeuf Illusion

• Height of 4-story building overestimated Height of 4-story building overestimated by approximately 25%by approximately 25%


Tufte’s Graphical IntegrityTufte’s Graphical Integrity

• Some lapses intentional, some not Some lapses intentional, some not • Lie Factor = size of effect in graph Lie Factor = size of effect in graph

size of effect in datasize of effect in data• Misleading uses of areaMisleading uses of area• Misleading uses of perspectiveMisleading uses of perspective• Leaving out important contextLeaving out important context• Lack of taste and aetheticsLack of taste and aethetics

Marti Hearst SIMS 247 SIMS 247 Lecture 12 Visual Properties and Visualization February 26, 1998.

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