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Feature Level Processing Lessons from low-level vision Applications in Highlighting Icon (symbol) design Glyph design.

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Presentation on theme: "Feature Level Processing Lessons from low-level vision Applications in Highlighting Icon (symbol) design Glyph design."— Presentation transcript:

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2 Feature Level Processing Lessons from low-level vision Applications in Highlighting Icon (symbol) design Glyph design

3 Spotfire product

4 Visual symbols

5 Architecture for visual thinking

6 Primitives of Perception (the phonemes). The whole visual field is processed in parallel This machinery tells us what kinds of information are easily distinguished Popout effects (general attention) Segmentation effects (dividing up the visual field)

7 Segmentation by Primitive Features

8 Livingston and Hubel Neural Architecture

9 Orientation and Size (Gabor primitives)

10 Image segmentation based on texture

11 Vector fields like using gabors

12 Pre-Attentive Processing

13 Color is Pre-Attentive (Pops out)

14 Generic Pre-Attentive Experiment Number of irrelevant items varies Pre-attentive 10 msec per item or better.

15 Color

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17 Orientation

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19 Motion

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21 Size

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23 Simple shading

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25 Conjunction (does not pop out)

26 Semantic Depth of Field

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28 Compound features (do not pop out)

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30 Surrounded colors do not pop out

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32 Laws of pre attentive display Must stand out on some simple dimension color, simple shape = orientation, size motion, depth Lessons for highlighting – one of each

33 Blinking momentarily attracts attention Lessons: Highlighting how to make information available to attention A flying box leads attention Using color Using underlining Blinking momentarily attracts attention Motion elicits an orienting response

34 More Pre-Attentive

35 Conjunction (does not pop out)

36 Pre-attentive conjunction

37 Conjunctions of motion and shape do pop out. (color also?) McLeod, P., Driver, J. and Crisp, J. (1988) Visual search for a conjunction of movement and form is parallel. Nature 332, 154-155. Driver, J., MacLeod, P. and Dienes, Z. (1992) Motion coherence and conjunction search: Implications for guided search theory. Perception and Psychophysics. 51, 1, 79-85.

38 MEGraph: Experimental system Allows for various topological range highlighting methods Goal from 30 to 2000 nodes MEGraph

39 Pre-Attentive Channels Form (orientation/size) Color Simple motion/blinking Addition/numerosity (up to 3) Spatial, stereo depth, shading, position

40 Pre-Attentive Conjunctions Stereo and color Color and motion Color and position Shape and position In general: spatial location and some aspect of form

41 Pre-Attentive Lessons Rapid visual search (10 msec/item) Easy to attend to Makes symbols distinct Based on simple visual attributes Faces, etc are not pre-attentive

42 Designing symbols

43 Perceptual Channels Color (3) Shape (size, orient) Motion (2?) Texture (2++) Position (x,y)

44 Spatial Channels Like interferes with like

45 Size contrast effect

46 Orient contrast

47 Size contrast effects can cause errors in information display

48 Chris Weigle: orientation channels for info display

49 Mapping data to display variables Data glyphs Position (2) Orientation (1) Size (spatial frequency) Motion (2)++ Blinking? Color (3) Note we have the problem of heterogeneity – There is no good solution Star glyph Method

50 Starplot glyph

51 Spotfire product

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56 Integral and Separable dimension (Garner) Can we read display attributes Independently Holistically Speeded classification task. Sort into two piles on one dimension or another

57 Separable Integral Motion Color red-greenyellow-blue black-whitered-green x-size y-size Color orientation

58 Lessons for Information Display Orthogonality - use a different channel for a different type of information If you need this use separable challenge If you need to highlight by two properties use separable dimensions.

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60 The programmable filter We can only look for patterns of simple features Conjunctions of shape, color cannot be programmed for parallel search of field Conjunctions of depth/motion and color/shape can be Integral dimensions tend to be seen holistically cannot be separated Separable dimensions tend to be seen separately

61 Searchlight Model of Attention

62 Searchlight Properties Size varies with data density Size varies with stress level Attention operators work within the searchlight beam Attention is a tunable filter Eye movements 3/sec – A series of saccades


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