The Art and Science of Depiction Vision Solves...
Transcript of The Art and Science of Depiction Vision Solves...
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The Art and Science of Depiction
Fredo DurandMIT- Lab for Computer Science
Vision Solves Problems
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Intro to Visual Perception 2
Plan• Vision as an cognitive process
• Computational theory of vision• Constancy, invariants
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Intro to Visual Perception 3
Distal vs. proximal stimulus• Distal stimulus: reality
• Proximal stimulus: retinal image
Distal stimulus (3D)proximal stimulus (2D)
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Intro to Visual Perception 4
Vision as an inverse problem• The distal stimulus is projected into a proximal
stimulus
Distal stimulus (3D)proximal stimulus (2D)
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Intro to Visual Perception 5
Vision as an inverse problem• The distal stimulus is projected into a proximal
stimulus
• How can we inverse this projection?
Distal stimulus (3D)proximal stimulus (2D)
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Intro to Visual Perception 6
Unconscious inference (Helmholtz)• Our vision system solves a problem
• Under-constrained problem– A visible point A’ can correspond to an infinity of 3D
points (A1, A2, A, A3…)
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Intro to Visual Perception 7
Unconscious inference (Helmholtz)• Our vision system solves a problem
• Under-constrained problem• Assumptions on the scene
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Intro to Visual Perception 8
The Ames room• Invalid assumption
• Wrong conclusions
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Intro to Visual Perception 9
Ames chair• Different scenes
• Same projection• We assume
it is a chair
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Intro to Visual Perception 10
Patrick Hughes• Perspective painting on the inverse geometry
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Intro to Visual Perception 11
The paradox of vision• Available information: proximal stimulus
• Conscious information: distal stimulus
Distal stimulus (3D)proximal stimulus (2D)
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Intro to Visual Perception 12
The paradox of Pictures• Distal vs. proximal
• Available information: proximal stimulus• Conscious information: distal stimulus
Distal stimulus (2D/3D)proximal stimulus (2D)
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Intro to Visual Perception 13
Pictures and inverse problem• Can
– Simplify analysis
– Be a puzzle
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Intro to Visual Perception 14
Plan• Vision as an cognitive process
• Computational theory of vision• Constancy, invariants
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Intro to Visual Perception 15
Vision as information processing• Input: retinal image
• Output: 3D layout, object recognition, etc.
Retinalimage
IntermediateData
Sceneunderstanding
Processing Processing
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Intro to Visual Perception 16
Computational theory of vision• Marr’s stages (extended by Palmer et al.)
• Human and Computer Vision• Classification of different kinds of processes
• Has proved fruitful in art studies
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Intro to Visual Perception 17
Computational theory of vision• Marr’s stages (extended by Palmer et al.)
• Human and Computer Vision• Classification of different kinds of processes
• Has proved fruitful in art studies
View-centered Object-centered
Extrinsic Intrinsic
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Intro to Visual Perception 18
Retinal image• Intensity
Cup
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Intro to Visual Perception 19
Retinal image• Intensity: hard to comprehend
Cup
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Intro to Visual Perception 20
Retinal image• Intensity
Cup
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Intro to Visual Perception 21
Image-based (primary sketch)• Contrast, edge detection
Cup
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Intro to Visual Perception 22
Image-based (primary sketch)• Contrast, edge detection
• Not so easy
Cup
Raw edge detection
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Intro to Visual Perception 23
Image-based (primary sketch)• Contrast, edge detection
Cup
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Intro to Visual Perception 24
Surface-based• Visible surfaces, organization
• Distance, orientation
Cup
Local orientation
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Intro to Visual Perception 25
Surface-based• Visible surfaces, organization
• Distance, orientation
Cup
Local orientation
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Intro to Visual Perception 26
Surface-based• Visible surfaces, organization
• Distance, orientation
Cup
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Intro to Visual Perception 27
Surface-based• Visible surfaces, organization
• Distance, orientation
Cup
Local orientation
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Intro to Visual Perception 28
Object-based• 3D properties, structure
• Nature of the description highly discussed
Cup
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Intro to Visual Perception 29
Category-based• Recognition, category, function
Cup
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Intro to Visual Perception 30
Feedback• Bottom-up and top-bottom
Cup
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Intro to Visual Perception 31
Scope of the theory• Computer Vision
• Human Vision• No direct correspondence in the brain
• Has proved fruitful conceptual tool
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Intro to Visual Perception 32
Relation to children drawing• First children draw what they know
– Object-centered
• Then, what they see– View-centered
Age 5 Age 9 (gifted!)
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Intro to Visual Perception 33
Evolution of children’s drawings• Asked to draw a table
Child’s view
7.4 9.7
11.9 13.6
14.3 13.7
Class of drawing & average age
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Intro to Visual Perception 34
What about adults?• Reproduce two drawing with similar angles
• Wheel:– Accuracy ~5°
• Street:– Error: 32 °
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Intro to Visual Perception 35
Drawing reproduction• From Drawing on the right side of the brain• Reproduction of Picasso’s portrait of Stravinsky
Original Regular reproduction Performed upside-down
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Intro to Visual Perception 36
Relation to pictures• How we see pictures
• Different classes of pictures for different stages
View-centered Object-centered
Extrinsic Intrinsic
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Intro to Visual Perception 37
Relation to pictures• Different classes of pictures for different stages
• Not a strict classification
View-centered Object-centered
Extrinsic Intrinsic
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Intro to Visual Perception 38
Relation to pictures• Chinese painting refuse extrinsic, only essential
• No shadow
View-centered Object-centered
Extrinsic Intrinsic
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Intro to Visual Perception 39
Retinal image• Impressionism
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Intro to Visual Perception 40
Retinal image• Impressionism
• Photography
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Intro to Visual Perception 41
Image-based• Line Drawing
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Intro to Visual Perception 42
Intermediate• View-based
• Cues for surface-basedfeature extractionare enhanced– Depth cues– Orientation cues
• No subjective feature(e.g. lighting)
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Intro to Visual Perception 43
Intermediate• View-based
• Cues for surface-basedfeature extractionare enhanced– Depth cues– Orientation cues
• More subjective feature(lighting)
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Intro to Visual Perception 44
Higher level• Primitive art
• Cubism• Schema
• “What I know”
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Intro to Visual Perception 45
Higher level• Primitive art
• Cubism• Schema
• “What I know”
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Intro to Visual Perception 46
Higher level• Primitive art
• Cubism• Schema
• “What I know”
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Intro to Visual Perception 47
Higher level• Primitive art
• Cubism• Schema
• “What I know”
• Not limited to picture
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Intro to Visual Perception 48
Expressionism• “What I feel”
Othermode
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Intro to Visual Perception 49
Relation with 2D/3D qualities• Almost the opposite!
• 3D quality correspond to retinal image• 2D quality arises from higher-level pictures
• Because of vision paradox– Distal is seen when proximal is shown
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Intro to Visual Perception 50
Relation with 2D/3D qualities• 3D quality but Retinal image
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Intro to Visual Perception 51
Relation with 2D/3D qualities• 2D quality but
Higher level
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Intro to Visual Perception 52
Further reading
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Intro to Visual Perception 53
Plan• Vision as an cognitive process
• Computational theory of vision• Constancy, invariants
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Intro to Visual Perception 54
Constancy & Invariants• We see intrinsic properties of objects
• They are “invariant” or “constant”• Ecological advantage
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Intro to Visual Perception 55
Visual angle vs. size• We see cylinders
with same size
• Valid most of the time
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Intro to Visual Perception 56
Visual angle vs. size• Mirror experiment:
– Draw your face on a mirror
– Measure: the drawing is ½ your face– However, you see “full size”
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Intro to Visual Perception 57
Visual angle vs. size• How do we do that?
– Distance
– Familiarity– Assumptions
• Here– Perspective– Position on ground plane– Similarity
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Intro to Visual Perception 58
Brightness vs. lightness• Brightness: subjective amount of light
• Lightness: how “white”
The white cells in shadow are as darkas the black illuminated cells
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Lightness constancy
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Lightness constancy• Sargent
• White in light and in shadow
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Color constancy• Chromaticity of light sources vary
• Chromatic adaptation– Similar to white balance on camcoder– Different films, filters
Objective colorsunder neon lighting
With chromaticadaptation
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Constancy• Size
• Lightness• Color
• Position
• Orientation• Shape
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Degree of constancy• Not always perfect
• Sometimes too much
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Degree of size constancy• The Moon illusion
– The Moon appears bigger on the horizon
– Because it looks farther (Emmert’s law)– Because references
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Degree of color constancy• Incandescent light looks warmer
• Sodium lighting looks yellowish• Depends on intensity
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Constancy & Pictures• Estimate size of depicted objects
• Different virtual viewpoints
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Constancy & Pictures• Estimate slant of depicted objects
• Different real viewing angles
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Importance of frame• Estimate slant of depicted objects
• Different real viewing angles, invisible frame
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Constancy & Pictures• Hybrid constancy with respect to
– Picture object
– Depicted scene
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Constancy & Pictures• Hybrid constancy
• Problem• Richness
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Degree of constancy• Vermeer Soldier and a Laughing Girl• Too good to be true: use of camera obscura
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Size constancy failure
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Size constancy failure
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Size constancy failure
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Breaking size constancy for symbol• Middle-age
• Size = social importance
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Size constancy dissonance• Surrealism (Magritte)
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Color constancy and pictures• Chromatic adaptation with respect to picture
object, not with respect to dicted scene
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Constancy & architecture• Palazzo Spada
in Rome(by Boromini)
• Short corridor• Column size
decreases
• Appears longer
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Constancy & Make Up
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Constancy & Lighting
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Next session• Gestalt and picture organization
• Gaze movement and focal point
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Assignments• Piranesi
– Tutorial 1 to 4
• Reading– Art and Illusion, Gombrich
– Summary 1 to 2 pages– 2 Discussion issues
• Feedback, 1 picture
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Discussion• The Man Who Mistook his Wife for a Hat• The Colorblind Painter• Oliver Sacks