Recap of Last (Last) Week

Transcription

1 Recap of Last (Last) Week 1

2 The Beauty of Information Visualization Napoléon s Historical Retreat 2

3 Course Design Homepage: have you visited and registered? 3

4 The Value of Information Visualization Have you looked at our reading materiel? John Snow s Investigation Through Visualization 4

5 A Random Reading Test 5

6 A Random Reading Test 6

7 Perception for Information Visualization IV Course Spring 14 Graduate Course of UCAS Mar. 14th,

8 InfoVis Pipeline Visualization User Data Perception 8

9 How Many 3 s? 9

10 How Many 3 s? 10

11 Foreground/Background g Color Effect We can see the yellow better than the blue 11

12 Hermann Grid Illusion 12

13 Rubin s Vase 13

14 Wind Map p( (2012) 14

15 Outline Human Perception Apparatus and Mechanism The eye and visual perception system Resolution, brain pixel and the optimal display size Color and luminance perception High-level Perception Theory Pre-attentive and attentive perception Magnitude perception 15

16 Human Visual Perception Model 16

17 Stage I: Parallel Processing to Extract Features Rapid parallel processing Features: orientation, color, texture, movements Bottom-up building: like an iconic store Pre-attentive features and attentive features 17

18 Stage II: Pattern Processing Slower serial processing Attention plays a key role Divide visual field into regions and simple patterns Different channels for object recognition (static) and guided motions (action) 18

19 Stage III: Visual Working Memory Highest level of perception Objects held in the working memory after active attention Only a few objects are held 19

20 Feature Integrations and Attention Parallel pre-attentive processing into the feature maps Serial search on the master map of the integrated features by active attention 20

21 Human Eye and Attention Rods and Cones as receptors Largest cone density at the Fovea Only a 2 degree field 21

22 The Visual Field and Resolution 22

23 23

24 Optimal Display Size Display Pixel (DP) and Brain Pixel (BP) Unique Stimulated Brain Pixel (USBP) = BP Redundant BP Display Efficiency i (DE) = USBP/DP 24

25 Optimal Display Size Under Same Display Pixels (Resolution) Why IMAX (Cinema) is still so cool to watch? Ultra-high resolution (display pixel): ~12800x

26 Color Perception Gray Scale Color Vision Gray scale (luminance) is critical for visual perception p The strict-sense color perception is only useful for a few scenarios, including information visualization 26

27 Color Perception Colored Treemap (encoding the stock price changes) Contour Map (e.g. temp.) 27

28 Trichromacy Theory: Color Spaces Human cones have three different color receptors (S/M/L) It explains why we have three-color spaces (RGB, CIE, etc.) Chicken have 12 color receptors 28

29 Color Mixture and RGB Space 29

30 Standard CIE Color Space 30

31 Color Perception after Visual Processing: The Opponent Process Theory Three basic color opponents (channels) I. black-white (luminance, due to all cone signals) II. Red-Green (between long and middle wavelength cone signals) III. Yellow-Blue (between short and long/middle wavelength signals) 31

32 Evidence: Cross-culture Color Naming Color namings are in the same order for most cultures 32

33 HSV Color Space Hue increasing Saturation increasing Value Luminance 33

34 Categorical (Uniquely Named) Colors Which is green? (0, 255, 0) (0, 255, 32) 34

35 Categorical (Uniquely Named) Colors Which is yellow? (255, 255, 0) (255, 223, 0) 35

36 Luminance Perception True Color Luminance Only 36

37 Luminance Perception Luminance = 0 Luminance = 1 37

38 Contrast Effect of Luminance/Brightness/Lightness (128,128,128) 128) (144,144,144) 144) Which is brighter? 38

39 Contrast Effect of Luminance/Brightness/Lightness (134,134,134) 134) (128,128,128) 128) Which is brighter? 39

40 Contrast Effect Contrast of luminance has an enhanced effect Ratios more important than magnitude Actual luminance Perceived luminance 40

41 Hermann Grid Illusion 41

42 Pre-attentive Perception Many features are processed fast and in parallel: pop out without thinking pre-attentive visual features 42

43 Pre-attentive Perception Object detection with Color Hue 43

44 Pre-attentive Perception Color Hue on Shape 44

45 Pre-attentive Perception Color Luminance 45

46 Pre-attentive Perception Shape 46

47 Pre-attentive Perception Boundary Detection 47

48 Pre-attentive Perception Line orientation Line length Line width Size Curvature Spatial grouping Blur Added marks Numerosity (one, two, or three objects) Color Hue Intensity Motion Flicker Direction of motion Spatial position Two-dimensional position Stereoscopic depth Convex/concave shape from shading 48

49 Pre-attentive Perception Fast for many pattern processing tasks under distractions: Pattern detection Boundary search 49

50 Pre-attentive Perception Combined Features, Conjunction Target Find a red circle? Generally slower: require a serial search 50

51 Magnitude Perception How to encode magnitude Color hue Luminance Font size Which visual features are good for encoding magnitude? 51

52 Magnitude Perception Compare Size Compare Length 1 : 4 for both cases How well does a visual feature encode magnitude? 52

53 Magnitude Perception The amount of lightness in a bright day beach is times larger than a dim lit room 53

54 Magnitude Perception Sensation (I) Exponent (P) Loudness 0.6 Brightness Smell Taste Temperature Vibration Duration 1.1 Pressure 1.1 Heaviness 1.45 Electic Shock (dark) 0.5 (point source) 0.55 (Coffee) 0.6 (Heptane) 0.6 (Saccharine) 1.3 (Salt) 1.0 (Cold) 1.6 (Warm) 0.6 (250 Hz) 0.95 (60 Hz) org/wiki/stevens' _power_law Psychophysical functions for various stimuli Steven s Law: S = I P 54

55 Magnitude Perception on Visual Encoding 55

56 Magnitude Scaling S = 0.98A

57 Magnitude Scaling 57

58 Summary Human Perception Apparatus and Mechanism The eye and visual perception system Resolution, brain pixel and the optimal display size Color and luminance perception High-level Perception Theory Pre-attentive and attentive perception Magnitude perception 58

59 Questions? What s Next -- Lecture 5: Visualization Design I 59

60 Announcement Students list 72 Students in total: double check on the list Check in before class or in the break, also record Q&A and resource bonus QQ group TBD Course site registration i 41 students have registered in the wiki You can t hand in your homework if not registered 60