4. What is color?
• Color is a psychological property of our visual experiences when we
look at objects and lights, not a physical property of those objects or
lights (S. Palmer, Vision Science: Photons to Phenomenology)
• Color is the result of interaction between physical light in the
environment and our visual system
5. Electromagnetic spectrum
• Why do we see light at these (visible) wavelengths?
Because that’s where the sun radiates electromagnetic energy
6. The Physics of Light
• Any source of light can be completely described physically by its
spectrum: the amount of energy emitted (per time unit) at each
wavelength 400 - 700 nm
7. Interaction of light and surfaces
• The color of surfaces is a result of a large variety of mechanisms,
including differential absorption at different wavelengths, refraction,
diffraction, and bulk scattering
10. The Human Eye
• Diameter: 20 mm
• 3 membranes enclose the eye
• Cornea & sclera
• Choroid
• Lens
• Retina
• The human eye is a camera:
photoreceptor cells (rods and
cones) in the retina have the
same function as
film/CCD/CMOS sensor in a
camera
11. Light Receptors
• Cones
– Cones are located in the fovea and
are sensitive to color
– Each one is connected to its own
nerve end
– Cone vision is called photopic (or
bright-light vision)
• Rods
– Rods are giving a general, overall
picture of the field of view and are
not involved in color vision
– Several rods are connected to a
single nerve and are sensitive to
low levels of illumination (scotopic
or dim-light vision)
12. Human Color Vision
• Unlike rods, which contain a single photopigment, there are three
types of cones that differ in the photopigment they contain
• Each of these photopigments has a different sensitivity to light of
different wavelengths
• For this reason are referred to as “blue,” “green,” and “red,” or, more
appropriately, short (S), medium (M), and long (L) wavelength cones
13. Color Perception
• Rods and cones act as filters on
the spectrum
• To get the output of a filter,
multiply its response curve by
the spectrum, integrate over all
wavelengths
• Each cone yields one number
• The sensitivity of the human eye
to luminous intensities related to
the three primary colors is not
the same
17. Standardizing color experience
• Accurate color reproduction is
commercially valuable
• Many products are identified by
color (e.g. “golden” arches)
• Only few color names are widely
recognized by English speakers:
– About 10; other languages have
fewer/more, but not many more.
– It’s common to disagree on
appropriate color names.
• Color reproduction problems
increased by prevalence of
digital imaging
18. Color Matching Experiment
• Most studies of the three-color
nature of human vision are
based on some variation of this
simple apparatus
• One part of the screen is
illuminated by a lamp of a target
color; the other by a mixture of
three colored lamps.
• The test subject (observer)
adjusts the intensities of the
three lamps until the mixture
appears to match the target
color.
19. Trichromacy
• Three numbers seem to be sufficient for encoding color
• In color matching experiments, most people can match any given light
with three primaries
• Exception: color blindness
• For the same light and same primaries, most people select the same
weights
• Trichromatic color theory dates back to 18th century (Thomas Young)
20. Additive vs. Subtractive
Color Matching
Additive
• Many colors can be represented
as a mixture of A, B, C:
M=a A + b B + c C
• Where the = sign should be read
as “matches”
• Gives a color description system:
two people who agree on A, B, C
need only supply (a, b, c) to
describe a color
Subtractive
• Some colors can’t be matched
like this M=a A + b B + c C,
instead, must write:
M+a A = b B+c C
• Interpret this as (-a, b, c)
• Problem for building monitors:
Choose R, G, B such that
positive linear combinations
match a large set of colors
21. Grassman’s Laws
• Additive matching is linear
• If two test lights can be matched with the same set of
weights, then they match each other:
– Suppose A = u1 P1 + u2 P2 + u3 P3 and B = u1 P1 + u2 P2 + u3 P3.
Then A = B.
• If we mix two test lights, then mixing the matches will
match the result:
– Suppose A = u1 P1 + u2 P2 + u3 P3 and B = v1 P1 + v2 P2 + v3 P3.
Then A+B = (u1+v1) P1 + (u2+v2) P2 + (u3+v3) P3.
• If we scale the test light, then the matches get scaled
by the same amount:
– Suppose A = u1 P1 + u2 P2 + u3 P3.
Then kA = (ku1) P1 + (ku2) P2 + (ku3) P3.
22. Color Matching Functions
• Choose primaries, say A, B, C
• Given energy function, E(λ) what amounts of primaries will match it?
• For each wavelength, determine how much of A, of B, and of C is
needed to match light of that wavelength alone
a(λ)
b(λ)
c(λ)
• These are color matching functions
• Then the match is
a(l)E(l)dl
ò
{ }A+
b(l)E(l)dl
ò
{ }B+
c(l)E(l)dl
ò
{ }C
23. Linear Color Spaces: RGB
• Linear color space that uses
single wavelength primaries
(645.16 nm for R, 526.32 nm for
G, and 444.44 nm
• Available colors are usually
represented as a unit cube—
usually called the RGB cube—
whose edges represent the R, G,
and B weights
• Subtractive matching required
for some wavelengths
24. Linear color spaces: CIE XYZ
• Established in 1931 by the
International Commission on
Illumination
• Primaries are imaginary, but
matching functions are
everywhere positive
• 2D visualization:
draw (x,y), where
x = X/(X+Y+Z)
y = Y/(X+Y+Z)
25. Nonlinear color spaces:
Uniform color spaces
• Unfortunately, differences in x,y coordinates do not reflect perceptual
color differences
• CIE u’v’ is a projective transform of x,y to make the ellipses more
uniform
u' =
4x
-2x +12y+3
v' =
9y
-2x +12y+3
26. Nonlinear color spaces: HSV
• First described by Alvy Ray Smith in 1978, HSV seeks to depict
relationships between colors, and improve upon the RGB color model
• Perceptually meaningful dimensions:
Hue, Saturation, Value (Intensity)
28. The Use of Color
in Computer Vision
• Color histogram for indexing and retrieval
Swain and Ballard, Color Indexing, IJCV 1991
• Skin detection
M. Jones and J. Rehg, Statistical Color Models with Application to
Skin Detection, IJCV 2002
• Image segmentation and retrieval
C. Carson, S. Belongie, H. Greenspan, and Ji. Malik, Blobworld:
Image segmentation using Expectation-Maximization and its
application to image querying, ICVIS 1999
• Robot soccer
M. Sridharan and P. Stone, Towards Eliminating Manual Color
Calibration at RoboCup. RoboCup-2005: Robot Soccer World Cup
IX, Springer Verlag, 2006
29. Acknowledgment
Some of slides in this PowerPoint presentation are adaptation from
various slides, many thanks to:
1. D.A. Forsyth, Department of Computer Science, University of Illinois
at Urbana – Champaign (http://luthuli.cs.uiuc.edu/~daf/)
2. Professor William Hoff, Department of Electrical Engineering &
Computer Science (http://inside.mines.edu/~whoff/)
3. James Hays, Computer Science Department, Brown University,
(http://cs.brown.edu/~hays/)
