COLOR VISION © Stephen E. Palmer, 2002 COLOR VISION “The Color Story” is a prototype for Cognitive Science Contributions from: Physics (Newton) Philosophy

COLOR VISION

© Stephen E. Palmer, 2002

COLOR VISION

“The Color Story” is a prototype for Cognitive Science

Contributions from:

Physics (Newton)Philosophy (Locke)Art (Munsell)Psychophysics (Maxwell)Physiology (De Valois)Cognitive Psychology (Rosch)Neurology (Zeki)Linguistics (Lakoff)Cognitive Anthropology (Berlin & Kay)Computer Science (Zadeh)


COLOR VISION

“The Color Story” is a prototype for Cognitive Science

Contributions from: * Berkeley faculty

Physics (Newton)Philosophy (Locke)Art (Munsell)Psychophysics (Maxwell)Physiology (De Valois)Cognitive Psychology (Rosch)Neurology (Zeki)Linguistics (Lakoff)Cognitive Anthropology (Berlin & Kay)Computer Science (Zadeh)


The Physics of Light

Light: Electromagnetic energy whose wavelength is between 400 nm and 700 nm. (1 nm = 10 meter)-6

400 500 600 700

ELECTROMAGNETIC SPECTRUM

VISIBLE SPECTRUM

10-14 meters 106 meters

Wavelength (nm)

CosmicRays

GammaRays X-rays UV Infra-

RedMicro-waves TV RadioLight



.

# P

ho

ton

s

D. Normal Daylight

Wavelength (nm.)

B. Gallium Phosphide Crystal

400 500 600 700

# P

ho

ton

s

Wavelength (nm.)

A. Ruby Laser

400 500 600 700

400 500 600 700

# P

ho

ton

s

C. Tungsten Lightbulb

400 500 600 700

# P

ho

ton

s

Some examples of the spectra of light sources



Some examples of the reflectance spectra of surfaces

Wavelength (nm)

% P

hoto

ns R

efle

cted

Red

400 700

Yellow

400 700

Blue

400 700

Purple

400 700


The Psychophysical Correspondence

There is no simple functional description for the perceivedcolor of all lights under all viewing conditions, but …...

A helpful constraint: Consider only physical spectra with normal distributions

area

Wavelength (nm.)

# Photons

400 700500 600

mean

variance



Mean Hue

yellowgreenblue

# P

hoto

ns

Wavelength



Variance Saturation

Wavelength

high

medium

low

hi.

med.

low# P

hoto

ns



Area Brightness#

Pho

tons

Wavelength

B. Area Lightness

bright

dark


Physiology of Color Vision


Cones cone-shaped less sensitive operate in high light color vision

Rods rod-shaped highly sensitive operate at night gray-scale vision

Two types of light-sensitive receptors

cone

rod

The Microscopic View

http://www.iit.edu/~npr/DrJennifer/visual/retina.html

Rods and Cones in the Retina

What Rods and Cones Detect

Notice how they aren’t distributed evenly, and the rod is more sensitive to shorter wavelengths

Center / Surround• Strong activation in center,

inhibition on surround• The effect you get using these

center / surround cells is enhanced edges

top: the stimuli itselfmiddle: brightness of the

stimulibottom: response of the retina

• You’ll see this idea get used in Regier’s model

http://www-psych.stanford.edu/~lera/psych115s/notes/lecture3/figures1.html

How They Fire

• No stimuli: – both fire at base rate

• Stimuli in center: – ON-center-OFF-surround

fires rapidly– OFF-center-ON-surround

doesn’t fire• Stimuli in surround:

– OFF-center-ON-surround fires rapidly

– ON-center-OFF-surround doesn’t fire

• Stimuli in both regions:– both fire slowly

Theories of Color Vision

Two main algorithmic theories of color vision:


Trichromatic Theory (Palmer/Young/Helmholtz)

Hermann von Helmholtz

Opponent Process Theory (Hering)

Ewald Hering


.

400 450 500 550 600 650

RE

LAT

IVE

AB

SO

RB

AN

CE

(%

)

WAVELENGTH (nm.)

100

50

440

S

530 560 nm.

M L

Three kinds of cones: Absorption spectra

Implementation of Trichromatic theory


Opponent Processes: R/G = L-M G/R = M-L B/Y = S-(M+L) Y/B = (M+L)-S


Opponent-Process Cells in LGN (De Valois)


0

BL

Wavelength

FiringRate

max.

R+G-G+R-

400 500 600 7000

BL

Wavelength

max.

B+Y- Y+B-FiringRate

400 500 600 700

Implementation of opponent process theory(Similar color behavior in retinal ganglion cells)


Double Opponent Cells in V1


G+R-

G+R-

R+G-

R+G-

Red/Green

Y+B-

Y+B-

B+Y-

B+Y-

Blue/Yellow

Color Blindness

Not everybody perceives colors in the same way!

What numbers do you see in these displays?


Color Blindness

There are several forms of inherited variations of color vision.

Trichromatic (“normal”) color vision

Dichromatic color vision 2 forms of red-green color blindness 1 form of yellow-blue color blindness

Monochromatic color vision 4 forms

Various forms of “color weakness”


Color Blindness

What does the world look like to a color blind person?

NormalTrichromat

Protanope Deuteranope Tritanope© Stephen E. Palmer, 2002


Red

+

-

0Green

Red/Green Receptors

Blue/Yellow Receptors

Black/White Receptors

Yellow

+

-

0Blue

White

+

-

0Black

Opponent Process theory (Hering): All colors are combinations of responses in three underlyingbipolar systems (Red/Green, Blue/Yellow, Black/White).



Dual Process Theory (Hurvich & Jameson): The colorvision system contains two stages: an initial trichromaticstage and a later opponent-process stage.


Trichromaticstage

Opponent-Process stage

Dual Process Theory


A Dual Process Wiring Diagram


S M L

R+ G-

+ +- -

B+ Y-+

+

- -

G+Y+

Bk+

S-M-L

++

L-M -S+M+L -S-M-L M-L

W+ Bk-

S+M+L

++ -

-

MLML

S M L

W-

B- R-

Trichromatic Stage

Opponent Process Stage

COLOR VISION: Part 4


1. Color Constancy:Surface-based processing

2. Color Naming:Category-based processing

Color Constancy


Color Constancy: the ability to perceive theinvariant color of a surface despite ecologicalVariations in the conditions of observation.

Another inverse problem: Physics of light emission and surface reflection underdetermine perception of surface color

Color Constancy


ReflectanceSpectrum

(Rw)

LuminanceSpectrum

(Lw)

X =

(# Photons Emitted) X (# Photons Reflected)(% Photons Reflected) =

IlluminationSpectrum

(Iw)

% Photons

400 700

Rw

400 700

Daylight# Photons

Iw

# Photons

400 700

Lw

Color Constancy


400 700 400 700 400 700

X =DaylightA

400 700 400 700 400 700

X =

TungstenBulb

B

400 700 400 700 400 700

X =

Wavelength (nm.)

HeliumNeonLaser

C

ReflectanceSpectrum

(Rw)

LuminanceSpectrum

(Lw)

X =

(# Photons Emitted) X (# Photons Reflected)(% Photons Reflected) =

IlluminationSpectrum

(Iw)

Color Constancy


Two approaches to lightness constancy

Unconscious Inference (Helmholtz)

Luminance = Intensity * Reflectance

If you know L and I, you can solve for R!

Invariant Relations (Hering)

Luminance ratios are invariant with illumination

Color Constancy


Luminance ratio is invariant over illumination:

100

90

10

INDOORS

10,000

9,000

1,000

OUTDOORS

Luminance Ratio = 9:1 Luminance Ratio = 9:1

Color Constancy


What about absolute lightness?

How do we know what is white?

(How big is the anchor???)

The anchoring problem:

Anchoring heuristic: The lightest region is taken as white

Color Naming


Basic Color Terms (Berlin & Kay)

Criteria:

1. Single words -- not “light-blue” or “blue-green”

2. Frequently used -- not “mauve” or “cyan”

3. Refer primarily to colors -- not “lime” or “gold”

4. Apply to any object -- not “roan” or “blond”

Color Naming


BCTs in English

RedGreenBlueYellowBlackWhite

GrayBrownPurpleOrange*Pink

Color Naming


Five more BCTs in a study of 98 languages

Light-BlueWarmCoolLight-WarmDark-Cool

The WCS Color Chips

• Basic color terms:– Single word (not blue-green)– Frequently used (not mauve)– Refers primarily to colors (not lime)– Applies to any object (not blonde)

FYI:

English has 11 basic color terms

Results of Kay’s Color Study

If you group languages into the number of basic color terms they have, as the number of color terms increases, additional terms specify focal colors

Stage I II IIIa / IIIb IV V VI VII

W or R or Y W W W W W W

Bk or G or Bu R or Y R or Y R R R R

Bk or G or Bu G or Bu Y Y Y Y

Bk G or Bu G G G

Bk Bu Bu Bu

W Bk Bk Bk

R Y+Bk (Brown) Y+Bk (Brown)

Y R+W (Pink)

Bk or G or Bu R + Bu (Purple)

R+Y (Orange)

B+W (Grey)

Color Naming


Typical “developmental” sequence of BCTs

Light-warm

Dark-cool

(2 Terms)

White

Warm

Dark-cool

(3 Terms)

Black

Cool

White

Warm

(4 Terms)

Red

Yellow

White

Black

Cool

(5 Terms)

White

Red

Yellow

Black

Green

Blue

(6 Terms)

Color Naming


Studied color categories in two ways

Boundaries

Best examples

(Berlin & Kay)

Color Naming


MEMORY : Focal colors are remembered better than nonfocal colors.

LEARNING: New color categories centered on focal colors are learned faster.

Categorization: Focal colors are categorized more quickly than nonfocal colors.

(Rosch)

Color Naming

Deg

ree

of M

embe

rshi

p

FUZZY SETS AND FUZZY LOGIC (Zadeh)

0

1.0

0

"Green"

very

not-at-all

a little bit

sorta

Hue

extremely

Degree ofMembership

Fuzzy set theory (Zadeh)

A fuzzy logical model of color naming (Kay & Mc Daniel)


Color Naming


0

1

Degree ofMembership

Hue

Blue Green Yellow Red

focal blue

focalgreen

focalyellow

focal red

BlueGreen

Yellow

Red

Hue

0

1

Degree ofMembership

“Primary” color categories

Color Naming


“Primary” color categories

RedGreenBlueYellowBlackWhite

Color Naming


“Derived” color categories.

Hue

0

1 Yellow Red

Y R

U

Degree ofMembership

Hue

Hue

Orange

0

1

Degree ofMembership

Fuzzylogical“ANDf”

Color Naming


“Derived” color categories

Orange = Red ANDf YellowPurple = Red ANDf BlueGray = Black ANDf WhitePink = Red ANDf WhiteBrown = Yellow ANDf Black(Goluboi = Blue ANDf White)

Color Naming


“Composite” color categories

Fuzzylogical“ORf”

Hue

0

1 Yellow Red

Y RU Degree ofMembership

Hue

Warm = Red Orf YellowCool = Blue Orf GreenLight-warm = White Orf WarmDark-cool = Black Orf Cool

Color Naming

FUZZY LOGICAL MODEL OF COLOR NAMING (Kay & McDaniel)

RedGreenBlue

YellowBlackWhite

OrangePurpleBrownPinkGray

[Light-blue]

[Warm][Cool]

[Light-warm][Dark-cool]

PRIMARY DERIVED COMPOSITE

Only 16 Basic Color Terms in Hundreds of Languages:

1.0

0

1.0

00

Yellow Orange = Yellow ANDf Red Warm = Yellow ORf RED

De

gre

e o

f M

em

be

rsh

ip

(Fuzzy ANDf) (Fuzzy ORf)(Fuzzy sets)


Documents

COLOR VISION © Stephen E. Palmer, 2002 COLOR VISION “The Color Story” is a prototype for Cognitive Science Contributions from: Physics (Newton) Philosophy