Computational Vision and Picture - People | MIT...
Transcript of Computational Vision and Picture - People | MIT...
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Perceptual and Artistic Principles for
Effective Computer Depiction
Fredo DurandMIT- Lab for Computer Science
Computational Visionand Picture
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Perceptual and Artistic Principles for
Effective Computer Depiction
Fredo DurandMIT- Lab for Computer Science
![Page 3: Computational Vision and Picture - People | MIT CSAILpeople.csail.mit.edu/fredo/SIG02_ArtScience/siggraph...– Be a puzzle, a riddle 18 Plan • Vision as an cognitive process •](https://reader036.fdocuments.in/reader036/viewer/2022071014/5fcc4c508949f847ab01ae10/html5/thumbnails/3.jpg)
Perceptual and Artistic Principles for
Effective Computer Depiction
Fredo DurandMIT- Lab for Computer Science
Computational Visionand Picture
![Page 4: Computational Vision and Picture - People | MIT CSAILpeople.csail.mit.edu/fredo/SIG02_ArtScience/siggraph...– Be a puzzle, a riddle 18 Plan • Vision as an cognitive process •](https://reader036.fdocuments.in/reader036/viewer/2022071014/5fcc4c508949f847ab01ae10/html5/thumbnails/4.jpg)
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Plan• Vision as an cognitive process• Computational theory of vision• Complex mapping
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Distal vs. proximal stimulus• Distal stimulus: reality• Proximal stimulus: retinal image
Distal stimulus (3D)proximal stimulus (2D)
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Vision as an inverse problem• The distal stimulus is projected into a proximal
stimulus
Distal stimulus (3D)proximal stimulus (2D)
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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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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…)
AA1A’
A2 A3
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How assumptions help• Ames chair
– 3 differentscenes
– Same projection
– We assumeit is a chair
– Resolves ambiguity
– Can be wrong
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The Ames room• Invalid assumption
– Walls perpendicular• Wrong conclusions
– Men have different sizes
real footprint
illusoryfootprint
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Positive and hollow face• Both seen convex because hollow faces are rare!
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Constancy & architecture• Palazzo Spada
in Rome(by Boromini)
• Short corridor• Column size
decreases• Appears longer
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The paradox of vision• Available information: proximal stimulus• Conscious information: distal stimulus
Distal stimulus (3D)proximal stimulus (2D)
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Brightness vs. lightness• Brightness: subjective amount of light• Lightness: how “white”
The white cells in shadow are as darkas the black illuminated cells
Illusion by Ted Adelson
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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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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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Pictures and the inverse problem• Pictures can
– Simplify the analysis– Be a puzzle, a riddle
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Plan• Vision as an cognitive process• Computational theory of vision• Complex mapping
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Vision as information processing• Input: retinal image• Output: 3D layout, object recognition, etc.
Retinalimage
IntermediateData
Sceneunderstanding
Processing Processing
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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-centeredExtrinsic Intrinsic
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Retinal image• Intensity: hard to comprehend
Cup
Retinal Image
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Image-based (primary sketch)• Contrast, edge detection• Not so easy
Cup
Raw edge detection
Image-based processing
Retinal Image
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Surface-based• Visible surfaces, organization• Distance, orientation
Cup
Local orientation
Image-based processing
Retinal Image
Surface-based processing
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Surface-based• Visible surfaces, organization• Distance, orientation
Image-based processing
Retinal Image
Surface-based processing
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Object-based• 3D properties, structure• Nature of the description highly discussed
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
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Category-based• Recognition, category, function
Cup
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
Category-based processing
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Feedback• Bottom-up and top-bottom
Cup
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
Category-based processing
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Scope of the theory• Computer Vision• Human Vision• No direct correspondence in the brain• Has proved fruitful conceptual tool
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
Category-based processing
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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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Evolution of children’s drawings• Asked to draw a table• First, draw what they know• Later, what they see
Child’s view
7.4 9.7
11.9 13.6
14.3 13.7
Class of drawing & average age
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What about adults?• Reproduce two drawing with similar angles• Wheel:
– Accuracy ~5°• Street:
– Error: 32 °
• Because in thefirst case, they focus on the 3D (distal) interpretation
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Drawing reproduction• Drawing on the right side of the brain, Edwards• Advises to reproduce drawings upside down• Distal interpretation does not impede • Forgers often reproduce paintings upside-down
Original Picasso drawing Reproduction Reproduction upside-down
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Relation to pictures• Different classes of pictures for different stages• Not a strict classification
View-centered Object-centeredExtrinsic Intrinsic
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Relation to pictures• Chinese painting refuse extrinsic, only essential• No shadow
View-centered Object-centeredExtrinsic Intrinsic
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Retinal image• Turner• “My business
is to paint not what I know, but what I see”
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Retinal image• Impressionism
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
Category-based processing
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Retinal image• Impressionism• Photography
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
Category-based processing
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Image-based• Line Drawing• Rivera
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Intrinsic vs. Extrinsic• Visual angle vs. true size• Caravaggio:
Wrong geometricallybut looks good
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Intrinsic vs. Extrinsic• Visual angle vs. true size• Vermeer:
too accurate to be true!
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Intermediate• View-based• Cues for surface-based
feature extractionare enhanced– Depth cues– Orientation cues
• No subjective feature(e.g. lighting)
Image-based processing
Retinal Image
Surface-based processing
Object-based processing
Category-based processing
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Higher level• Primitive art• Cubism• Schema• “What I know”
Image-based processing
Retinal Image
Object-based processing
Category-based processing
Surface-based processing
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Higher level• Primitive art• Cubism• Schema• “What I know”
Image-based processing
Retinal Image
Object-based processing
Category-based processing
Surface-based processing
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Higher level• Primitive art• Cubism• Schema• “What I know”
Image-based processing
Retinal Image
Object-based processing
Category-based processing
Surface-based processing
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Expressionism• “What I feel”
Othermode
Image-based processing
Retinal Image
Object-based processing
Category-based processing
Surface-based processing
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Relation with 2D/3D emphasis• Almost the opposite!• 3D impression corresponds 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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Relation with 2D/3D qualities• 3D impression but Retinal image
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Relation with 2D/3D qualities• 2D emphasis but
Higher level
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Making pictures: inverse of inverse
• Previsualization (Adams)• Solving the direct problem is a good start, but…
Real scene
(possiblyimaginary)
Picture
Perception & CognitionMessage, goal
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Plan• Vision as an cognitive process• Computational theory of vision• Complex mapping
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3D and 2D attributes• [Willats 97]• Show coloured or numbered die to children (6-7) • The still draw a rectangle• But different colours or many points• The rectangle stands for the whole dice• The notion of 3D object with corners is translated
as a 2D object with corners
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Projection: Topographical• London underground• Metric properties are used
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Projection: Topological• Beck’s map of London underground, 1931• Only the connectedness and organization are
preserved• [Agrawala,
in this volume]
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Mapping of curvature• Convex: positive curvature
– 3D example: Egg– 2D: Convex contour
• Concave: negative curvature– 3D example: Interior of cup– 2D: Nothing, hidden contour
• Saddle: mix of positive and negative curvature– 3D example:Saddle (surprising!) – 2D: Concave contour
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Mapping of curvature• But some artists map 3D concave objects to 2D
concave outlines• This maps the property of concavity• The left view of the plate is more “correct” but
does not convey the notion of concavity
“projective” plate “mapped” plate
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Mapping of curvature• Small plate under the cup
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Mapping of curvature• Complex denotation• See [Durand, page 15]
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Further reading
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Further reading• Calvin & Hobbes by Watterson !