Digit al I mage Processing
Chapt er 6:
Color I mage Processing
Digit al I mage Processing
Chapt er 6:
Color I mage Processing
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Spectrum of White Light Spectrum of White Light
1666 Sir Isaac Newton, 24 year old, discovered white light spectrum.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Electromagnetic Spectrum Electromagnetic Spectrum
Visible light wavelength: from around 400 to 700 nm
1. For an achromatic (monochrome) light source,
there is only 1 attribute to describe the quality: intensity
2. For a chromatic light source, there are 3 attributes to describe
the quality:
Radiance = total amount of energy flow from a light source (Watts)
Luminance = amount of energy received by an observer (lumens)
Brightness = intensity
The Eye
Figure is from slides at
Gonzalez/ Woods DIP book
website (Chapter 2)
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Cross section illustration Cross section illustration
Two Types of Photoreceptors at Retina Two Types of Photoreceptors at Retina
Rods
Long and thin
Large quantity (~ 100 million)
Provide scotopic vision (i.e., dim light vision or at low illumination)
Only extract luminance information and provide a general overall picture
Cones
Short and thick, densely packed in fovea (center of retina)
Much fewer (~ 6.5 million) and less sensitive to light than rods
Provide photopic vision (i.e., bright light vision or at high illumination)
Help resolve fine details as each cone is connected to its own nerve end
Responsible for color vision
Mesopic vision
provided at intermediate illumination by both rod and cones
our interest
(well-lighted display)
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Sensitivity of Cones in the Human Eye Sensitivity of Cones in the Human Eye
6-7 millions cones
in a human eye
- 65% sensitive to Red light
- 33% sensitive to Green light
- 2 % sensitive to Blue light
Primary colors:
Defined CIE in 1931
Red = 700 nm
Green = 546.1nm
Blue = 435.8 nm
CIE = Commission Internationale de lEclairage
(The International Commission on Illumination)
Luminance vs. Brightness Luminance vs. Brightness
Luminance (or intensity)
Independent of the luminance of surroundings
I(x,y,) -- spatial light distribution
V() -- relative luminous efficiency func. of visual system ~ bell shape
(different for scotopic vs. photopic vision;
highest for green wavelength, second for red, and least for blue )
Brightness
Perceived luminance
Depends on surrounding luminance
Same lum.
Different
brightness
Different lum.
Similar
brightness
Luminance vs. Brightness (contd) Luminance vs. Brightness (contd)
Example: visible digital watermark
How to make the watermark
appears the same graylevel
all over the image?
from IBM Watson web page
Vatican Digital Library
Look into Simultaneous Contrast Phenomenon Look into Simultaneous Contrast Phenomenon
Human perception more sensitive to luminance
contrast than absolute luminance
Webers Law: | L
s
L
0
| / L
0
= const
Luminance of an object (L
0
) is set to be just noticeable
from luminance of surround (L
s
)
For just-noticeable luminance difference L:
L / L d( log L ) 0.02 (const)
equal increments in log luminance are perceived as equally different
Empirical luminance-to-contrast models
Assume L [1, 100], and c [0, 100]
c = 50 log
10
L (logarithmic law, widely used)
c = 21.9 L
1/3
(cubic root law)
Mach Bands
Visual system tends to undershoot or overshoot around the
boundary of regions of different intensities
Demonstrates the perceived brightness is not a simple
function of light intensity
Figure is from slides
at Gonzalez/ Woods
DIP book website
(Chapter 2)
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Color of Light Color of Light
Perceived color depends on spectral content (wavelength
composition)
e.g., 700nm ~ red.
spectral color
A light with very narrow bandwidth
A light with equal energy in all visible bands appears
white
Spectrum from http://www.physics.sfasu.edu/astro/color.html
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Primary and Secondary Colors Primary and Secondary Colors
Primary
color
Primary
color
Primary
color
Secondary
colors
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Primary and Secondary Colors (cont.) Primary and Secondary Colors (cont.)
Additive primary colors: RGB
use in the case of light sources
such as color monitors
Subtractive primary colors: CMY
use in the case of pigments in
printing devices
RGB add together to get white
White subtracted by CMY to get
Black
Representation by Three Primary Colors Representation by Three Primary Colors
Any color can be reproduced by mixing an appropriate set of
three primary colors (Thomas Young, 1802)
Three types of cones in human retina
Absorption response S
i
() has peaks around 450nm (blue), 550nm
(green), 620nm (yellow-green)
Color sensation depends on the spectral response {
1
(C),
2
(C),
3
(C) } rather than the complete light spectrum C()
S
1
() C() d
S
2
() C() d
S
3
() C() d
C()
color light
1
(C)
2
(C)
3
(C)
Identically
perceived colors
if
i
(C
1
) =
i
(C
2
)
Example: Seeing Yellow Without Yellow Example: Seeing Yellow Without Yellow
mix green and red light to obtain perception of
yellow, without shining a single yellow photon
520nm 630nm
570nm
=
Seeing Yellow figure is from B.Liu ELE330 S01 lecture notes @ Princeton;
R/G/B cone response is from slides at Gonzalez/ Woods DIP book website
Color Matching and Reproduction Color Matching and Reproduction
Mixture of three primaries: C = Sum(
k
P
k
() )
To match a given color C
1
adjust
k
such that
i
(C
1
) =
i
(C), i = 1,2,3.
Tristimulus values T
k
(C)
T
k
(C) =
k
/ w
k
w
k
the amount of k
th
primary to match the reference white
Chromaticity t
k
= T
k
/ (T
1
+T
2
+T
3
)
t
1
+t
2
+t
3
= 1
visualize (t
1
, t
2
) to obtain chromaticity diagram
Hue: dominant color corresponding to a dominant
wavelength of mixture light wave
Saturation: Relative purity or amount of white light mixed
with a hue (inversely proportional to amount of white
light added)
Brightness: Intensity
Color Characterization Color Characterization
Hue
Saturation
Chromaticity
amount of red (X), green (Y) and blue (Z) to form any particular
color is called tristimulus.
Perceptual Attributes of Color Perceptual Attributes of Color
Value of Brightness
(perceived luminance)
Chrominance
Hue
specify color tone (redness, greenness, etc.)
depend on peak wavelength
Saturation
describe how pure the color is
depend on the spread (bandwidth) of light
spectrum
reflect how much white light is added
RGB HSV Conversion ~ nonlinear
HSV circular cone is from online
documentation of Matlab image
processing toolbox
http://www.mathworks.com/access
/helpdesk/help/toolbox/images/col
or10.shtml
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(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
CIE Chromaticity Diagram CIE Chromaticity Diagram
Trichromatic coefficients:
Z Y X
X
x
+ +
Z Y X
Y
y
+ +
Z Y X
Z
z
+ +
1 + + z y x
x
y
Points on the boundary are
fully saturated colors
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Gamut of Color Monitors and Printing Devices Color Gamut of Color Monitors and Printing Devices
Color Monitors
Printing devices
CIE Color Coordinates (contd) CIE Color Coordinates (contd)
CIE XYZ system
hypothetical primary sources to yield all-positive spectral
tristimulus values
Y ~ luminance
Color gamut of 3 primaries
Colors on line C1 and C2 can be
produced by linear mixture of the two
Colors inside the triangle gamut
can be reproduced by three primaries
From http://www.cs.rit.edu/~ncs/color/t_chroma.html
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
RGB Color Model RGB Color Model
Purpose of color models: to facilitate the specification of colors in
some standard
RGB color models:
- based on cartesian
coordinate system
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
RGB Color Cube RGB Color Cube
R = 8 bits
G = 8 bits
B = 8 bits
Color depth 24 bits
= 16777216 colors
Hidden faces
of the cube
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
RGB Color Model (cont.) RGB Color Model (cont.)
Red fixed at 127
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Safe RGB Colors Safe RGB Colors
Safe RGB colors: a subset of RGB colors.
There are 216 colors common in most operating systems.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
RGB Safe RGB Safe- -color Cube color Cube
The RGB Cube is divided into
6 intervals on each axis to achieve
the total 6
3
= 216 common colors.
However, for 8 bit color
representation, there are the total
256 colors. Therefore, the remaining
40 colors are left to OS.
CMY and CMYK Color Models CMY and CMYK Color Models
C = Cyan
M = Magenta
Y = Yellow
K = Black
Primary colors for pigment
Defined as one that subtracts/absorbs a
primary color of light & reflects the
other two
CMY Cyan, Magenta, Yellow
Complementary to RGB
Proper mix of them produces black
1
1
1
]
1
1
1
1
]
1
1
1
1
]
1
B
G
R
Y
M
C
1
1
1
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
HSI Color Model HSI Color Model
RGB, CMY models are not good for human interpreting
HSI Color model:
Hue: Dominant color
Saturation: Relative purity (inversely proportional
to amount of white light added)
Intensity: Brightness
Color carrying
information
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Relationship Between RGB and HSI Color Models Relationship Between RGB and HSI Color Models
RGB
HSI
Hue and Saturation on Color Planes Hue and Saturation on Color Planes
1. A dot is the plane is an arbitrary color
2. Hue is an angle from a red axis.
3. Saturation is a distance to the point.
HSI Color Model (cont.) HSI Color Model (cont.)
Intensity is given by a position on the vertical axis.
HSI Color Model HSI Color Model
Intensity is given by a position on the vertical axis.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Example: HSI Components of RGB Cube Example: HSI Components of RGB Cube
Hue Saturation Intensity
RGB Cube
Converting Colors from RGB to HSI Converting Colors from RGB to HSI
'
>
G B
G B
H
if 360
if
[ ]
[ ]
'
+
+
2 / 1
2
1
) )( ( ) (
) ( ) (
2
1
cos
B G B R G R
B R G R
B G R
S
+ +
3
1
) (
3
1
B G R I + +
Converting Colors from HSI to RGB Converting Colors from HSI to RGB
) 1 ( S I B
1
]
1
+
) 60 cos(
cos
1
H
H S
I R
o
) ( 1 B R G +
RG sector: 120 0 < H GB sector:
240 120 < H
) 1 ( S I R
1
]
1
+
) 60 cos(
cos
1
H
H S
I G
o
) ( 1 G R B +
) 1 ( S I G
1
]
1
+
) 60 cos(
cos
1
H
H S
I B
o
) ( 1 B G R +
BR sector: 360 240 H
120 H H
240 H H
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Example: HSI Components of RGB Colors Example: HSI Components of RGB Colors
Hue
Saturation Intensity
RGB
Image
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Example: Manipulating HSI Components Example: Manipulating HSI Components
Hue
Saturation Intensity
RGB
Image
Hue Saturation
Intensity
RGB
Image
Color Coordinates Used in TV Transmission Color Coordinates Used in TV Transmission
Facilitate sending color video via 6MHz mono TV
channel
YIQ for NTSC (National Television Systems Committee)
transmission system
Use receiver primary system (R
N
, G
N
, B
N
) as TV receivers
standard
Transmission system use (Y, I, Q) color coordinate
Y ~ luminance, I & Q ~ chrominance
I & Q are transmitted in through orthogonal carriers at the same freq.
YUV (YCbCr) for PAL and digital video
Y ~ luminance, Cb and Cr ~ chrominance
Color Coordinates Color Coordinates
RGB of CIE
XYZ of CIE
RGB of NTSC
YIQ of NTSC
YUV (YCbCr)
CMY
Examples Examples
HSV
YUV
RGB
Examples Examples
RGB
HSV
YIQ
Summary Summary
Monochrome human vision
visual properties: luminance vs. brightness, etc.
image fidelity criteria
Color
Color representations and three primary colors
Color coordinates
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Color Image Processing Color Image Processing
There are 2 types of color image processes
1. Pseudocolor image process: Assigning colors to gray
values based on a specific criterion. Gray scale images to be processed
may be a single image or multiple images such as multispectral images
2. Full color image process: The process to manipulate real
color images such as color photographs.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Pseudocolor Image Processing Pseudocolor Image Processing
Why we need to assign colors to gray scale image?
Answer: Human can distinguish different colors better than different
shades of gray.
Pseudo color = false color : In some case there is no color concept
for a gray scale image but we can assign false colors to an image.
Intensity Slicing or Density Slicing Intensity Slicing or Density Slicing
'
>
T y x f C
T y x f C
y x g
) , ( if
) , ( if
) , (
2
1
Formula:
C
1
= Color No. 1
C
2
= Color No. 2
T
Intensity
C
o
l
o
r
C
1
C
2
T 0
L-1
A gray scale image viewed as a 3D surface.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Intensity Slicing Example Intensity Slicing Example
An X-ray image of a weld with cracks
After assigning a yellow color to pixels with
value 255 and a blue color to all other pixels.
Multi Level Intensity Slicing Multi Level Intensity Slicing
k k k
l y x f l C y x g <
) , ( for ) , (
1
C
k
= Color No. k
l
k
= Threshold level k
Intensity
C
o
l
o
r
C
1
C
2
0
L-1
l
1
l
2
l
3
l
k
l
k-1
C
3
C
k-1
C
k
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Multi Level Intensity Slicing Example Multi Level Intensity Slicing Example
k k k
l y x f l C y x g <
) , ( for ) , (
1
C
k
= Color No. k
l
k
= Threshold level k
An X-ray image of the Picker
Thyroid Phantom.
After density slicing into 8 colors
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Coding Example Color Coding Example
Gray-scale image of average
monthly rainfall.
Color coded image
South America region
Gray
Scale
Color
map
0
10
>20
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Gray Level to Color Transformation Gray Level to Color Transformation
Assigning colors to gray levels based on specific mapping functions
Red component
Green component
Blue component
Gray scale image
(Images from Rafael C.
Gonzalez and Richard
E. Wood, Digital Image
Processing, 2
nd
Edition.
Gray Level to Color Transformation Example Gray Level to Color Transformation Example
An X-ray image of a
garment bag with a
simulated explosive
device
An X-ray image
of a garment bag
Color
coded
images
Transformations
(Images from Rafael C.
Gonzalez and Richard
E. Wood, Digital Image
Processing, 2
nd
Edition.
Gray Level to Color Transformation Example Gray Level to Color Transformation Example
An X-ray image of a
garment bag with a
simulated explosive
device
An X-ray image
of a garment bag
Color
coded
images
Transformations
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Pseudocolor Coding Pseudocolor Coding
Used in the case where there are many monochrome images such as multispectral
satellite images.
Pseudocolor Coding Example Pseudocolor Coding Example
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Pseudocolor Coding Example Pseudocolor Coding Example
Washington D.C. area
Visible blue
= 0.45-0.52 m
Max water penetration
Visible green
= 0.52-0.60 m
Measuring plant
Visible red
= 0.63-0.69 m
Plant discrimination
Near infrared
= 0.76-0.90 m
Biomass and shoreline mapping
1 2
3 4
Red =
Green =
Blue =
Color composite images
1
2
3
Red =
Green =
Blue =
1
2
4
Better visualization Show quite
clearly the difference between
biomass (red) and human-made features.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Pseudocolor Coding Example Pseudocolor Coding Example
Psuedocolor rendition
of Jupiter moon Io
A close-up
Yellow areas = older sulfur deposits.
Red areas = material ejected from
active volcanoes.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Basics of Full Basics of Full- -Color Image Processing Color Image Processing
2 Methods:
1. Per-color-component processing: process each component separately.
2. Vector processing: treat each pixel as a vector to be processed.
Example of per-color-component processing: smoothing an image
By smoothing each RGB component separately.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Example: Example: Full Full- -Color Image and Variouis Color Space Components Color Image and Variouis Color Space Components
Color image
CMYK components
RGB components
HSI components
Color Transformation Color Transformation
Formulation:
[ ] ) , ( ) , ( y x f T y x g
f(x,y) = input color image, g(x,y) = output color image
T = operation on f over a spatial neighborhood of (x,y)
When only data at one pixel is used in the transformation, we
can express the transformation as:
) , , , (
2 1 n i i
r r r T s K i= 1, 2, , n
Where r
i
= color component of f(x,y)
s
i
= color component of g(x,y)
Use to transform colors to colors.
For RGB images, n = 3
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Example: Color Transformation Example: Color Transformation
) , ( ) , (
) , ( ) , (
) , ( ) , (
y x kr y x s
y x kr y x s
y x kr y x s
B B
G G
R R
Formula for RGB:
) , ( ) , ( y x kr y x s
I I
Formula for CMY:
) 1 ( ) , ( ) , (
) 1 ( ) , ( ) , (
) 1 ( ) , ( ) , (
k y x kr y x s
k y x kr y x s
k y x kr y x s
Y Y
M M
C C
+
+
+
Formula for HSI:
These 3 transformations give
the same results.
k = 0.7
I H,S
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Complements Color Complements
Color complement replaces each color with its opposite color in the
color circle of the Hue component. This operation is analogous to
image negative in a gray scale image.
Color circle
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Complement Transformation Example Color Complement Transformation Example
Color Slicing Transformation Color Slicing Transformation
'
1
]
1
>
otherwise
2
if 5 . 0
1
i
n j any
j j
i
r
W
a r
s
We can perform slicing in color space: if the color of each pixel
is far from a desired color more than threshold distance, we set that
color to some specific color such as gray, otherwise we keep the
original color unchanged.
i= 1, 2, , n
or
( )
'
>
otherwise
if 5 . 0
1
2
0
2
i
n
j
j j
i
r
R a r
s
Set to gray
Keep the original
color
Set to gray
Keep the original
color
i= 1, 2, , n
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Slicing Transformation Example Color Slicing Transformation Example
Original image
After color slicing
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Tonal Correction Examples Tonal Correction Examples
In these examples, only
brightness and contrast are
adjusted while keeping color
unchanged.
This can be done by
using the same transformation
for all RGB components.
Power law transformations
Contrast enhancement
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Balancing Correction Examples Color Balancing Correction Examples
Color imbalance: primary color components in white area
are not balance. We can measure these components by
using a color spectrometer.
Color balancing can be
performed by adjusting
color components separately
as seen in this slide.
Histogram Equalization of a Full Histogram Equalization of a Full- -Color Image Color Image
v Histogram equalization of a color image can be performed by
adjusting color intensity uniformly while leaving color unchanged.
v The HSI model is suitable for histogram equalization where only
Intensity (I) component is equalized.
k
j
j
k
j
j r k k
N
n
r p r T s
0
0
) ( ) (
where r and s are intensity components of input and output color image.
(Images from Rafael C.
Gonzalez and Richard E.
Wood, Digital Image
Processing, 2
nd
Edition.
Histogram Equalization of a Full Histogram Equalization of a Full- -Color Image Color Image
Original image
After histogram
equalization
After increasing
saturation component
Color Image Smoothing Color Image Smoothing
2 Methods:
1. Per-color-plane method: for RGB, CMY color models
Smooth each color plane using moving averaging and
the combine back to RGB
2. Smooth only Intensity component of a HSI image while leaving
H and S unmodified.
1
1
1
1
1
1
1
]
1
xy
xy
xy
xy
S y x
S y x
S y x
S y x
y x B
K
y x G
K
y x R
K
y x
K
y x
) , (
) , (
) , (
) , (
) , (
1
) , (
1
) , (
1
) , (
1
) , ( c c
Note: 2 methods are not equivalent.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Image Smoothing Example (cont.) Color Image Smoothing Example (cont.)
Color image
Red
Green
Blue
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Image Smoothing Example (cont.) Color Image Smoothing Example (cont.)
Hue Saturation Intensity
Color image
HSI Components
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Image Smoothing Example (cont.) Color Image Smoothing Example (cont.)
Smooth all RGB components Smooth only I component of HSI
(faster)
Color Image Smoothing Example (cont.) Color Image Smoothing Example (cont.)
Difference between
smoothed results from 2
methods in the previous
slide.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Image Sharpening Color Image Sharpening
We can do in the same manner as color image smoothing:
1. Per-color-plane method for RGB,CMY images
2. Sharpening only I component of a HSI image
Sharpening all RGB components Sharpening only I component of HSI
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Image Sharpening Example (cont.) Color Image Sharpening Example (cont.)
Difference between
sharpened results from 2
methods in the previous
slide.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Segmentation Color Segmentation
2 Methods:
1. Segmented in HSI color space:
A thresholding function based on color information in H and S
Components. We rarely use I component for color image
segmentation.
2. Segmentation in RGB vector space:
A thresholding function based on distance in a color vector space.
Color Segmentation in HSI Color Space Color Segmentation in HSI Color Space
Hue
Saturation Intensity
Color image
1 2
3 4
(Images from Rafael C.
Gonzalez and Richard E.
Wood, Digital Image
Processing, 2
nd
Edition.
Color Segmentation in HSI Color Space (cont.) Color Segmentation in HSI Color Space (cont.)
Product of and
5 6
7 8
5 2
Binary thresholding of S component
with T = 10%
Histogram of 6 Segmentation of red color pixels
Red pixels
(Images from Rafael C.
Gonzalez and Richard E.
Wood, Digital Image
Processing, 2
nd
Edition.
Color Segmentation in HSI Color Space (cont.) Color Segmentation in HSI Color Space (cont.)
Color image Segmented results of red pixels
(Images from Rafael C.
Gonzalez and Richard E.
Wood, Digital Image
Processing, 2
nd
Edition.
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Segmentation in RGB Vector Space Color Segmentation in RGB Vector Space
1. Each point with (R,G,B) coordinate in the vector space represents
one color.
2. Segmentation is based on distance thresholding in a vector space
'
>
T y x D
T y x D
y x g
T
T
) ), , ( ( if 0
) ), , ( ( if 1
) , (
c c
c c
c
T
= color to be segmented.
c(x,y) = RGB vector at pixel (x,y).
D(u,v) = distance function
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Example: Segmentation in RGB Vector Space Example: Segmentation in RGB Vector Space
Color image
Results of segmentation in
RGB vector space with Threshold
value
Reference color c
T
to be segmented
box the in pixel of color average
T
c
T = 1.25 times the SD of R,G,B values
In the box
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Gradient of a Color Image Gradient of a Color Image
Since gradient is define only for a scalar image, there is no concept
of gradient for a color image. We cant compute gradient of each
color component and combine the results to get the gradient of a color
image.
Red Green Blue
Edges
We see
4 objects.
We see
2 objects.
Gradient of a Color Image (cont.) Gradient of a Color Image (cont.)
One way to compute the maximum rate of change of a color image
which is close to the meaning of gradient is to use the following
formula: Gradient computed in RGB color space:
[ ]
2
1
2 sin 2 2 cos ) ( ) (
2
1
) (
'
+ + +
xy yy xx yy xx
g g g g g F
( )
1
1
]
1
yy xx
xy
g g
g 2
tan
2
1
1
2 2 2
x
B
x
G
x
R
g
xx
2 2 2
y
B
y
G
y
R
g
yy
y
B
x
B
y
G
x
G
y
R
x
R
g
xy
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Obtained using
the formula
in the previous
slide
Sum of
gradients of
each color
component
Original
image
Difference
between
2 and 3
2
3
2 3
Gradient of a Color Image Example Gradient of a Color Image Example
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Gradients of each color component
Red Green Blue
Gradient of a Color Image Example Gradient of a Color Image Example
Noise in Color Images Noise in Color Images
Noise can corrupt each color component independently.
(Images from Rafael C.
Gonzalez and Richard E.
Wood, Digital Image
Processing, 2
nd
Edition.
Noise is less
noticeable
in a color
image
AWGN
2
=800 AWGN
2
=800
AWGN
2
=800
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Noise in Color Images Noise in Color Images
Hue Saturation Intensity
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Noise in Color Images Noise in Color Images
Hue
Saturation Intensity
Salt & pepper noise
in Green component
(Images from Rafael C. Gonzalez and Richard E.
Wood, Digital Image Processing, 2
nd
Edition.
Color Image Compression Color Image Compression
JPEG2000 File
Original image
After lossy compression with ratio 230:1
