Designing Applications that See Designing Applications that See - PowerPoint PPT Presentation

stanford hci group / cs377s Designing Applications that See Designing Applications that See Lecture 2: Human Vision and Perception Dan Maynes-Aminzade 10 January 2008 10 January 2008 Designing Applications that See http://cs377s.stanford.edu

R Reminders i d � Fill out the online course sign-up sheet Fill t th li i h t � Assignment #1 released next Tuesday, due g y, one week later � Remember to check the course calendar for � R b t h k th l d f the latest readings, and the course home page for announcements Lecture 2: Human Vision 10 January 2008 2

Why Are People Taking CS377S? Why Are People Taking CS377S? � “I haven't taken any computer vision courses to date so � I haven t taken any computer vision courses to date, so I'm interested in learning some basics.” � “I've heard great things about it from previous students, and I've always wanted to take a computer vision course, d l d k but have been scared away by the theory.” � “I want to build a dance interface! ” I want to build a dance interface! � “It seems like a good application of my past Computer Vision and Graphics coursework, and I've always wanted t t k to take an HCI-type course.” HCI t ” � “Webcams are unlike any other input device, so I'm hoping that learning to make use of them will inspire new hoping that learning to make use of them will inspire new design opportunities.” � “Because Monzy's gonna rap the lectures.” Lecture 2: Human Vision 10 January 2008 3

T d Today’s Goals ’ G l � Learn how human visual processing works L h h i l i k � Compare human vision to computer vision p p � Understand the limits and constraints of h human vision i i � Discuss some relationships between vision, p perception, and cognition Lecture 2: Human Vision 10 January 2008 4

O tli Outline � Overview of visual system O i f i l t � Constraints of human visual processing p g � Shortcuts, “hacks,” and illusions � Vision and cognition d Lecture 2: Human Vision 10 January 2008 5

A B d M d l f H A Bad Model of Human Vision Vi i 2. Images sent to 3. Brain updates 1. Eye captures scene brain for processing model of world 4. React and repeat loop Lecture 2: Human Vision 10 January 2008 6

P Problems with this Model bl ith thi M d l 1. Eyes are not passive receptors; vision is an E t i t i i i interactive process. Lecture 2: Human Vision 10 January 2008 7

P Problems with this Model bl ith thi M d l 1. Eyes are not passive receptors; vision is an E t i t i i i interactive process. 2. Processing is not serial, and reactions and decisions are made at different stages decisions are made at different stages. Lecture 2: Human Vision 10 January 2008 8

P Problems with this Model bl ith thi M d l 1. Eyes are not passive receptors; vision is an E t i t i i i interactive process. 2. Processing is not serial, and reactions and decisions are made at different stages decisions are made at different stages. 3. We see a complex world, not just colors, shapes, and motion. Lecture 2: Human Vision 10 January 2008 9

Th R ti The Retina (courtesy of National Eye Institute) Lecture 2: Human Vision 10 January 2008 10

Th F The Fovea (courtesy of Brain Connection) Lecture 2: Human Vision 10 January 2008 11

B hi d th E Behind the Eyes Lecture 2: Human Vision 10 January 2008 12

I th Vi In the Visual Cortex l C t Lecture 2: Human Vision 10 January 2008 13

H Hypercolumns l Lecture 2: Human Vision 10 January 2008 14

P Processing Streams i St Lecture 2: Human Vision 10 January 2008 15

P Processing Streams i St Lecture 2: Human Vision 10 January 2008 16

Hi h Higher-Order Functions O d F ti Lecture 2: Human Vision 10 January 2008 17

R Resolution Limits l ti Li it 1 8 0 ° 1 8 0 Ret ina Fovea 4 ° -highest hi h t density Eye of cones Lecture 2: Human Vision 10 January 2008 18

F Fovea Demo D Lecture 2: Human Vision 10 January 2008 19

F Foveal Eye Chart l E Ch t (courtesy of Stuart Anstis) Lecture 2: Human Vision 10 January 2008 20

C l Color at the Periphery t th P i h (courtesy of Exploratorium) Lecture 2: Human Vision 10 January 2008 21

Ph t Photoreceptor Distribution t Di t ib ti Lecture 2: Human Vision 10 January 2008 22

Aside: Why Do Pirates Wear Eyepatches? Aside: Why Do Pirates Wear Eyepatches? Human Vision vs. Computer Vision 5 September 2007 23

(courtesy of Jason Harrison)

(courtesy of Jason Harrison)

(courtesy of Jason Harrison)

(courtesy of Jason Harrison)

(courtesy of Jason Harrison)

(courtesy of Jason Harrison)

Constructing a Seamless Whole C t ti S l Wh l (courtesy of Stuart Anstis) Lecture 2: Human Vision 10 January 2008 30

S Saccades d (courtesy of John M. Henderson) Lecture 2: Human Vision 10 January 2008 31

E Eye Tracking T ki (courtesy of Poynter Institute) Lecture 2: Human Vision 10 January 2008 32

R Reading Saccades di S d Lecture 2: Human Vision 10 January 2008 33

Th Bli d S The Blind Spot t (courtesy of Peter Kaiser) Lecture 2: Human Vision 10 January 2008 34

Ch Cheshire Cat Illusion hi C t Ill i (courtesy of Exploratorium) Lecture 2: Human Vision 10 January 2008 35

S Saccadic Suppression di S i � You can see someone else’s eyes shifting… Y l ’ hifti � But when you look in a mirror, you can’t see y , y your own eyes move! � This may help some magic � Thi h l i tricks work – a wave with one hand captures your gaze, and meanwhile you g y miss what the other hand is doing doing. Lecture 2: Human Vision 10 January 2008 36

St Stopped Clock Illusion d Cl k Ill i Lecture 2: Human Vision 10 January 2008 37

Sh Shape from Shading f Sh di (courtesy of Dorothy Kleffner) Lecture 2: Human Vision 10 January 2008 38

Sh Shape From Shading F Sh di (courtesy of Dorothy Kleffner) Lecture 2: Human Vision 10 January 2008 39

Sh Shape from Shading f Sh di (courtesy of Dorothy Kleffner) Lecture 2: Human Vision 10 January 2008 40

P Pop-Out Effect O t Eff t (courtesy of Dorothy Kleffner) Lecture 2: Human Vision 10 January 2008 41

R Real Life Example l Lif E l Lecture 2: Human Vision 10 January 2008 42

R Real-Life Example l Lif E l (courtesy of Susan Kare) Lecture 2: Human Vision 10 January 2008 43

R Real-Life Example l Lif E l Lecture 2: Human Vision 10 January 2008 44

R Real-Life Example l Lif E l (courtesy of Stuart Anstis) Lecture 2: Human Vision 10 January 2008 45

Moving Object or Changing Lighting? Moving Object or Changing Lighting? (courtesy of D. Kersten) Lecture 2: Human Vision 10 January 2008 46

Moving Object or Changing Lighting? Moving Object or Changing Lighting? (courtesy of D. Kersten) Lecture 2: Human Vision 10 January 2008 47

D Depth Perception th P ti Lecture 2: Human Vision 10 January 2008 48

D Depth Cues th C � Bi � Binocular cues l � Stereoscopic depth � Perspective-based cues P b d � Size gradient, texture gradient � Occlusion-based cues � Object overlap, cast shadows j p � Focus-based cues � Atmospheric perspective, object intensity Atmospheric perspective, object intensity � Motion-based cues � Parallax � Parallax Lecture 2: Human Vision 10 January 2008 49

P Perspective Cue Example ti C E l (courtesy of Herman Bollman) Lecture 2: Human Vision 10 January 2008 50

Si Size Cue Example C E l Lecture 2: Human Vision 10 January 2008 51

At Atmospheric Cue Example h i C E l (courtesy of Daniel Weiskopf) Lecture 2: Human Vision 10 January 2008 52

I t Intensity Cue Example it C E l Lecture 2: Human Vision 10 January 2008 53

B i ht Brightness versus Luminance L i � Which square is brighter, A or B? Whi h i b i ht A B? (courtesy of Edward Adelson) Lecture 2: Human Vision 10 January 2008 54

B i ht Brightness versus Luminance L i � They are the same! Th th ! (courtesy of Edward Adelson) Lecture 2: Human Vision 10 January 2008 55

Aft Aftereffect Illusions ff t Ill i Lecture 2: Human Vision 10 January 2008 56

Aft Aftereffect Illusions ff t Ill i Lecture 2: Human Vision 10 January 2008 57

P Perception of Motion ti f M ti Lecture 2: Human Vision 10 January 2008 58

P Perception of Motion ti f M ti Lecture 2: Human Vision 10 January 2008 59

M ti Motion Extrapolation E t l ti � The “Flash-Lag” Effect Th “Fl h L ” Eff t Lecture 2: Human Vision 10 January 2008 60

M ti Motion Detection D t ti � “Stepping Feet” Illusion “St i F t” Ill i Lecture 2: Human Vision 10 January 2008 61

D f Defense Hardware H d Mark Leung’s “Crazy Computer Bug” Mark Leungs Crazy Computer Bug Lecture 2: Human Vision 10 January 2008 62