Computer Vision
-
Color
Hanyang
University
Jong
-
Il Park
Department of Computer Science and Engineering
,
Hanyang
University
Topics to be covered
Light and Color
Color Representation
Color Discrimination
Application
Department of Computer Science and Engineering
,
Hanyang
University
The visible light spectrum
We “see” electromagnetic radiation in a range of wavelengths
Department of Computer Science and Engineering
,
Hanyang
University
Relative sizes
Department of Computer Science and Engineering
,
Hanyang
University
Light spectrum
The appearance of light depends on its power
spectrum
How much power (or energy) at each wavelength
daylight
tungsten bulb
Our visual system converts a light spectrum into “color”
This is a rather complex transformation
Department of Computer Science and Engineering
,
Hanyang
University
The human visual system
Color perception
Light hits the retina, which contains photosensitive cells
rods and cones
These cells convert the spectrum into a few discrete
values
Department of Computer Science and Engineering
,
Hanyang
University
Density of rods and cones
Rods and cones are
non
-
uniformly
distributed on the retina
Rods responsible for intensity, cones responsible for color
Fovea
-
Small region (1 or 2
°
) at the center of the visual field
containing the highest density of cones (and no rods).
Less visual acuity in the periphery
—
many rods wired to the same neuron
light
Cone
Rod
Retina
Department of Computer Science and Engineering
,
Hanyang
University
8
Rods: Twilight Vision
130
million rod cells per eye.
1000 times more sensitive to
light than cone cells.
Most to green light (about
550
-
555 nm), but with a
broad range of response
throughout the visible
spectrum.
Produces relatively blurred
images, and in shades of
gray.
Pure rod vision is also called
twilight vision
.
Relative neural response of rods as
a function of light wavelength.
400
500
600
700
Wavelength (nm)
1.00
0.75
0.50
0.25
0.00
Relative response
Department of Computer Science and Engineering
,
Hanyang
University
9
Cones: Color Vision
7
million cone cells per eye.
Three types of cones* (S, M, L),
each "tuned" to different maximum
responses at:
-
S : 430 nm (blue)
(2%)
M: 535 nm (green) (33%)
L : 590 nm (red) (65%)
Produces sharp, color images.
Pure cone vision is called
photopic
or color vision.
Spectral absorption of light by
the three cone types
400
500
600
700
Wavelength (nm)
1.00
0.75
0.50
0.25
0.00
Relative absorbtion
S
M
L
*
S = Short wavelength cone
M = Medium wavelength cone
L = Long wavelength cone
Department of Computer Science and Engineering
,
Hanyang
University
Color perception
Three types of cones
Each is sensitive in a different region of the spectrum
Different
sensitivities: we are more sensitive to green than red
varies from person to person (and with age)
Colorblindness
—
deficiency in at least one type of cone
L response curve
Department of Computer Science and Engineering
,
Hanyang
University
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
Q: How can we represent an entire spectrum with 3 numbers?
S
M
L
Wavelength
Power
A: We can’t! Most of the information is lost.
As a result, two different spectra may appear indistinguishable
•
such spectra are known as
metamers
•
http://www.cs.brown.edu/exploratories/freeSoftware/repository/edu/brown/cs/explo
ratories/applets/spectrum/metamers_guide.html
Department of Computer Science and Engineering
,
Hanyang
University
Eye Color Sensitivity
Although cone response
is similar for the L, M, and
S cones, the number of the
different types of cones
vary.
L:M:S = 40:20:1
Cone responses typically
overlap for any given
stimulus, especially for the
M
-
L cones.
The human eye is most
sensitive to green light.
Spectral absorption of light by
the three cone types
400
500
600
700
Wavelength (nm)
1.00
0.75
0.50
0.25
0.00
Relative absorbtion
S
M
L
S, M, and L cone distribution in the fovea
Effective sensitivity of cones
(log plot)
400
500
600
700
Wavelength (nm)
1.00
0.1
0.01
0.001
0.0001
Relative sensitivity
S
M
L
Department of Computer Science and Engineering
,
Hanyang
University
Theory of Trichromatic Vision
The principle that the color
you see depends on signals
from the three types of cones
(L, M, S).
The principle that visible color
can be mapped in terms of the
three colors (R, G, B) is called
trichromacy
.
The three numbers used to
represent the different
intensities of red, green, and
blue needed are called
tristimulus values
.
=
Tristimulus values
r
g
b
Department of Computer Science and Engineering
,
Hanyang
University
Seeing Colors
The colors we perceive
depends on:
-
Illumination
source
Illumination source
Object
reflectance
factor
Object reflectance
Observer
spectral
sensitivity
Observer response
Observer
response
=
Tristimulus values
(Viewer response)
r
g
b
x
x
The product of these three
factors will produce the
sensation of color.
Department of Computer Science and Engineering
,
Hanyang
University
Additive Colors
Start with Black
–
absence of any
colors. The more colors added,
the brighter it gets.
Color formation by the addition of
Red, Green, and Blue, the three
primary colors
Examples of additive color usage:
-
Human eye
Lighting
Color monitors
Color video cameras
Additive color wheel
Department of Computer Science and Engineering
,
Hanyang
University
Subtractive Colors
Starts with a white background
(usually paper).
Use Cyan, Magenta, and/or
Yellow dyes to subtract from
light reflected by paper, to
produce all colors.
Examples of Subtractive color
use:
-
Color printers
Paints
Subtractive color wheel
Department of Computer Science and Engineering
,
Hanyang
University
Using Subtractive Colors on Film
Color absorbing pigments are layered on each other.
As white light passes through each layer, different
wavelengths are absorbed.
The resulting color is produced by subtracting
unwanted colors from white.
White
light
Pigment layers
Reflecting layer
(white paper)
M
Y
C
B
R
G
K
W
Green
Red
Blue
Black
White
Cyan
Yellow
Magenta
Cyan
Magenta
Yellow
Black
Department of Computer Science and Engineering
,
Hanyang
University
380
480
580
680
780
Wavelength (nm)
0
9
Relative power
The dashed line represents daylight reflected from sunflower, while
the solid line represents the light emitted from the color monitor
adjusted to match the color of the sunflower.
Metamerism
Spectrally different lights
that simulate cones
identically appear identical.
Such colors are called
color
metamers
.
This phenomena is called
metamerism
.
Almost all the colors that
we see on computer
monitors are
metamers
.
Department of Computer Science and Engineering
,
Hanyang
University
The Mechanics of
Metamerism
Under
trichromacy
, any color
stimulus can be matched by a
mixture of three primary stimuli.
Metamers
are colors having the
same
tristimulus
values
R
,
G
, and
B
; they will match color stimulus
C
and will appear to be the same
color.
Wavelength (nm)
780
380
480
580
680
0
9
Relative power
The two metamers look the same because
they have similar
tristimulus
values
.
Wavelength (nm)
780
380
480
580
680
0
9
Relative power
Wavelength (nm)
780
380
480
580
680
0
9
Relative power
780
380
780
380
780
380
R S r d
G S g d
B S b d
Department of Computer Science and Engineering
,
Hanyang
University
Gamut
A gamut is the range of
colors that a device can
render, or detect.
The larger the gamut,
the more colors can be
rendered or detected.
A large gamut implies a
large color space.
0
0
0.2
0.4
0.6
0.8
0.2
0.4
0.6
0.8
x
y
Human vision
gamut
Monitor
gamut
Photographic
film gamut
Department of Computer Science and Engineering
,
Hanyang
University
Color Spaces
A Color Space is a method by which colors are
specified, created, and visualized.
Colors are usually specified by using three attributes,
or coordinates, which represent its position within a
specific color space.
These coordinates do not tell us what the color looks
like, only where it is located within a particular color
space.
Color models are 3D coordinate systems, and a
subspace within that system, where each color is
represented by a single point.
Department of Computer Science and Engineering
,
Hanyang
University
Color Spaces
Color Spaces are often geared towards specific
applications or hardware.
Several types:
-
HSI (Hue, Saturation, Intensity) based
RGB (Red, Green, Blue) based
CMY(K) (Cyan, Magenta, Yellow, Black) based
CIE based
Luminance
-
Chrominance based
CIE: International Commission on Illumination
Department of Computer Science and Engineering
,
Hanyang
University
RGB*
One of the simplest color models.
Cartesian coordinates for each
color; an axis is each assigned to
the three primary colors red (R),
green (G), and blue (B).
Corresponds to the principles of
additive colors
.
Other colors are represented as an
additive mix of R, G, and B.
Ideal for use in computers.
*
Red, Green, and Blue
Black
(0,0,0)
Cyan
(0,1,1)
Green
(0,1,0)
Yellow
(1,1,0)
Red
(1,0,0)
Magenta
(1,0,1)
Blue
(0,0,1)
White
(1,1,1)
RGB Color Space
Department of Computer Science and Engineering
,
Hanyang
University
RGB Image Data
Red Channel
Green Channel
Full Color Image
Blue Channel
Department of Computer Science and Engineering
,
Hanyang
University
CMY(K)*
Main color model used in the
printing industry. Related to RGB.
Corresponds to the principle of
subtractive colors
, using the
three secondary colors Cyan,
Magenta, and Yellow.
Theoretically, a uniform mix of
cyan, magenta, and yellow
produces black (center of
picture). In practice, the result is
usually a dirty brown
-
gray tone.
So black is often used as a fourth
color.
*
Cyan, Magenta, Yellow, (and
blacK
)
Magenta
Yellow
Cyan
Blue
Red
Green
Black
White
Producing other colors from subtractive colors.
Department of Computer Science and Engineering
,
Hanyang
University
CMY Image Data
Full Color Image
Cyan Image (1
-
R)
Magenta Image (1
-
G)
Yellow Image (1
-
B)
Department of Computer Science and Engineering
,
Hanyang
University
CMY
–
RBG Transformation
The following matrices will perform transformations between
RGB and CMY color spaces.
Note that:
-
R
= Red
G
= Green
B
= Blue
C
= Cyan
M
= Magenta
Y
= Yellow
All values for
R, G, B
and
C, M, Y
must first
be normalized.
1
1
1
C R
M G
Y B
1
1
1
R C
G M
B Y
Department of Computer Science and Engineering
,
Hanyang
University
HSI / HSL / HSV*
Very similar to the way human visions see color.
Works well for natural illumination, where hue changes
with brightness.
Used in machine color vision to identify the color of
different objects.
Image processing applications like histogram operations,
intensity transformations, and convolutions operate on
only an image's intensity and are performed much
easier on an image in the HSI color space.
*
H=Hue, S = Saturation, I (Intensity) = B (Brightness), L = Lightness, V = Value
Department of Computer Science and Engineering
,
Hanyang
University
HSI Color Space
Hue
What we describe as the color of
the object.
Hues based on RGB color space.
The hue of a color is defined by
its counterclockwise angle from
Red (0
°
); e.g. Green = 120
°
, Blue
= 240
°
.
RGB Color Space
RGB cube viewed from
gray
-
scale axis
RGB cube
viewed from
gray
-
scale axis,
and rotated 30
°
HSI Color
Wheel
Red 0
º
Gre
en
120
º
Blue
240
º
Saturation
Degree to which hue differs from
neutral gray.
100% = Fully saturated, high
contrast between other colors.
0% = Shade of gray, low contrast.
Measured radially from intensity
axis.
0
%
Saturation
100
%
Department of Computer Science and Engineering
,
Hanyang
University
HSI Color Space
Intensity
Brightness of each Hue, defined by
its height along the vertical axis.
Max saturation at 50% Intensity.
As Intensity increases or decreases
from 50%, Saturation decreases.
Mimics the eye response in nature;
As things become brighter they look
more pastel until they become
washed out.
Pure white at 100% Intensity. Hue
and Saturation undefined.
Pure black at 0% Intensity. Hue
and Saturation undefined.
Hue
Saturation
0%
100%
Intensity
100%
0%
Department of Computer Science and Engineering
,
Hanyang
University
HSI Image Data
Hue Channel
Saturation Channel
Intensity Channel
Full Image
Department of Computer Science and Engineering
,
Hanyang
University
CIE L*a*b* Color Space / CIELAB
Second of two systems adopted by CIE in
1976 as models that better showed
uniform color spacing in their values.
Based on the earlier (1942)
color
opposition system
by Richard Hunter
called L, a, b.
Very important for desktop color.
Basic color model in Adobe PostScript
(level 2 and level 3)
Used for color management as the device
independent model of the ICC* device
profiles.
CIE L*a*b* color axes
*
International Color Consortium
Department of Computer Science and Engineering
,
Hanyang
University
CIE L*a*b* (cont’d)
Central vertical axis
: Lightness (L*),
runs from 0 (black) to 100 (white).
a
-
a' axis
: +a values indicate amounts of
red,
-
a values indicate amounts of green.
b
-
b' axis
, +b indicates amounts of yellow;
-
b values indicates amounts of blue. For
both axes, zero is neutral gray.
Only values for two color axes (a*, b*)
and the lightness or grayscale axis (L*)
are required to specify a color.
CIELAB Color difference,
E*
ab
, is
between two points is given by:
+
a
-
a
-
b
+
b
100
0
L*
CIE L*a*b* color axes
(L
1
*, a
1
*, b
1
*)
(L
2
*, a
2
*, b
2
*)
2 2 2
* ( *) ( *) ( *)
ab
E L a b
Department of Computer Science and Engineering
,
Hanyang
University
CIELAB Image Data
Full Color Image
L data
L
-
a channel
L
-
b channel
Department of Computer Science and Engineering
,
Hanyang
University
Scene
Radiance L
Lens
Image
Irradiance E
Camera
Electronics
Scene
Image
Irradiance E
Measured
Pixel Values, I
Non
-
linear Mapping!
Linear Mapping!
•
Before light hits the image plane:
•
After light hits the image plane:
Can we go from measured pixel value, I, to scene radiance, L?
Relationship between Scene and Image Brightness
Department of Computer Science and Engineering
,
Hanyang
University
Demosaicking
Cf. 3CCD camera
Department of Computer Science and Engineering
,
Hanyang
University
The camera response function relates image irradiance at the image plane
to the measured pixel intensity values.
Camera
Electronics
Image
Irradiance E
Measured
Pixel Values, I
I
E
g
:
(Grossberg and Nayar)
Relation between Pixel Values I and Image Irradiance E
Department of Computer Science and Engineering
,
Hanyang
University
•
Important preprocessing step for many vision and graphics algorithms such as
photometric stereo, invariants, de
-
weathering, inverse rendering, image based rendering, etc.
E
I
g
:
1
•
Use a color chart with precisely known reflectances.
Irradiance = const * Reflectance
Pixel Values
3.1%
9.0%
19.8%
36.2%
59.1%
90%
•
Use more camera exposures to fill up the curve.
•
Method assumes constant lighting on all patches and works best when source is
far away (example sunlight).
•
Unique inverse exists because
g
is monotonic and smooth for all cameras.
0
255
0
1
g
?
?
1
g
Radiometric Calibration
Department of Computer Science and Engineering
,
Hanyang
University
Dynamic Range
Department of Computer Science and Engineering
,
Hanyang
University
•
Dynamic Range: Range of brightness values measurable with a camera
(Hood 1986)
High Exposure Image
Low Exposure Image
•
We need 5
-
10 million values to store all brightnesses around us.
•
But, typical 8
-
bit cameras provide only 256 values!!
•
Today’s Cameras: Limited Dynamic Range
The Problem of Dynamic Range
Department of Computer Science and Engineering
,
Hanyang
University
High dynamic range imaging
Techniques
Debevec:
http://www.debevec.org/Research/HDR/
Columbia:
http://www.cs.columbia.edu/CAVE/tomoo/RRHomePage/rrgallery.html
Department of Computer Science and Engineering
,
Hanyang
University
Color Discrimination
Active approach
Using controlled lights
Passive approach
Using optical filters
camera
LED Cluster
controller
scene
illumination 1
Illumination 2
Department of Computer Science and Engineering
,
Hanyang
University
Visual effect of illumination
400
415
430
445
460
475
490
505
520
535
550
565
580
595
610
625
640
655
670
685
700
Spectral Reflectance
Cancer
Normal
400
415
430
445
460
475
490
505
520
535
550
565
580
595
610
625
640
655
670
685
700
Spectral Sensitivity
Wavelength(nm)
Camera
Blue
Channel
Camera
Green
Channel
Camera
Red
Channel
400
415
430
445
460
475
490
505
520
535
550
565
580
595
610
625
640
655
670
685
700
Spectral Power
Synthetic Illumination L
A
400
415
430
445
460
475
490
505
520
535
550
565
580
595
610
625
640
655
670
685
700
Spectral Power
Halogen Lamp
RGB Distance: 115.86
RGB Distance: 98.12
RGB Distance: 92.85
400
410
420
430
440
450
460
470
480
490
500
510
520
530
540
550
560
570
580
590
600
610
620
630
640
650
660
670
680
690
700
Spectral Power
Xenon Lamp
Department of Computer Science and Engineering
,
Hanyang
University
Optimal illumination
Department of Computer Science and Engineering
,
Hanyang
University
Imaging for Autonomous Vehicle
For traffic lights
Passive approach
Using optimized color filters
For
pedestrian detection
Multispectral/
hyperspectral
imaging
Infrared band
Department of Computer Science and Engineering
,
Hanyang
University
Segmentation
Keying
Interactive segmentation
[
서울대
]
Department of Computer Science and Engineering
,
Hanyang
University
Virtual Studio
•
NHK STRL: Synthevision, VS, DTPP (1989~1992)
VS Overview paper: S.Gibbs et al.(1996)
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