computational perceptionlewicki/cp-s08/perceptual-constancy1.pdf · cp08: perceptual constancy 1...

Computational Perception15-485/785

Perceptual Constancy 1

Michael S. Lewicki ◇ Carnegie MellonCP08: Perceptual Constancy 1

Perceptual Constancy

How do we perceive stimuli as being the same?

• Sensory patterns can be radically different.

• Shading is one example.

• What are others?

2

example from Dan Kersten


A schematic of visual computation

visual structure representation

Sensory Coding

perceptual constancy

object and scene representation

3

Perceptual constancy is inherently an inferential process that depends on both lower-level and higher-level information.


Lightness constancy

4

• Is the perception really constant?

• Is the retinal image accessible?

• Drawing well is hard, but recognizing poor drawings is easy

image from Ted Adelson


Perceptual constancy demos

5

Purves lab demos:www.purveslab.net/seeforyourself/

http://www.purveslab.net/seeforyourself/







Brightness contrast illusions

6

images from www.purveslab.net

http://www.purveslab.net



Brightness contrast with color

7





Effect of texture on contrast perception: Chubb Illusion

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More subtle types of invariance

9

Braje, Legge, and Kersten, 1999:

• Test object recognition with or without cast shadows

• Cast shadows have no effect on object recognition response time or accuracy.

• Same is true for color.

Attached Shadows

Cast Shadows


Size constancy

• People are very good at estimating actual size, esp. when multiple cues are available

• If cues are removed, they choose closer to retinal size.

• With depth cues, it is very difficult to judge retinal size.

10

from Rock, 1984

from Palmer, 1999


How big is the cylinder?

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Estimate the height of the third vs the first. from Palmer, 1999



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The third is 1/3 the height of the first. from Palmer, 1999



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The third is 1/3 the height of the first. from Palmer, 1999


Direct estimation of size: texture gradients

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Objects covering same number texture gradient units have same size.

from Rock, 1984from Goldstein, 2001


Relative estimation of size

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Relative size is invariant with distance.from Palmer, 1999


Shape constancy

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• Shapes are also perceived in their 3D form over many viewing positions.

• We have little access to the retinal shape.

• Common shapes are perceived accurately largely independent of projective transformation.

from Palmer, 1999


Even complex shapes are perceived accurately

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from Palmer, 1999


Perceptual shape constancy is limited

18

• For unfamiliar shapes, subjects to not generalize to novel views.

• Subjects’ memory was much worse when tested on a novel view compared to the same view.

• Subjects also show poor shape constancy on unfamiliar solid 3D shapes.

from Palmer, 1999


Shape constancy and familiar objects

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Subjects do show shape constancy for familiar objects.

from Palmer, 1999


Measuring the effect of view on recognition

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View is related to ratings of how well an image depicts object.

from Palmer, 1999


Canonical views of common objects

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Subjects are quickest to name object from canonical view.


Wiser (1981)

• With poor intrinsic axis (A), subjects recognition memory was much better when figure’s orientation was the same.

• With good intrinsic axis (B), subjects showed now difference when orientation was changed.

Reference frames

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from Palmer, 1999


Reference frames are not completely general

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Same or Different?

Now?

from Hancock, 2000


Context affects perception

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How do these faces differ?

Now can you tell?from Hancock, 2000


Generalized templates

25

Idea: Learn a basis for a specific class of images, e.g. faces (Turk and Pentland, 1991).

• Normalize object set by position, size, etc. Usually by hand.

• Model the objects using linear superposition:

• Derive optimal basis functions for object set

• Under a Gaussian model, this is PCA, which is feasible for large patterns.

Generalized templates

Idea: Learn a basis for a specific class of images, e.g. faces (Turk and Pentland,1991).

• Normalize object set by position, size, etc. Usually by hand.• Model the objects using linear superposition:

I(x, y) =!

i

ai!i(x, y)

• Derive optimal {!1, ...,!M} for object set.• under a Gaussian model this is PCA, which is feasible for large patterns.

Computational Perception and Scene Analysis, Mar 23, 2004 / Michael S. Lewicki, CMU !! !

!

? 25


Eigenfaces

from Hancock, 2000

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Eigen face distortions

from Hancock, 2000

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Face image principal components

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The face image components move back and forth along their axes.

from Hancock, 2000


Face shape principal components

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The face shape components move back and forth along their axes.

from Hancock, 2000


Making a caricature: step 1

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Identify positions of facial features. from Bruce and Young, 1998



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Distort positions from average. from Bruce and Young, 1998



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Morph original face according to caricature distortion.

from Bruce and Young, 1998


Computer-generated caricatures of famous actors

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Caricatures were more recognizable (i.e. faster RT) than originals.

from Bruce and Young, 1998

computational perceptionlewicki/cp-s08/perceptual-constancy1.pdf · cp08: perceptual constancy 1...

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