‫بسمه‌تعالي‬
Digital Image Processing
Color Image Processing
(Chapter 6)
H.R. Pourreza
H.R. Pourreza
Preview
 Motive
- Color is a powerful descriptor that often
simplifies object identification and extraction
from a scene.
- Human can discern thousands of color shades
and intensities, compared to about only two
dozen shades of gray.
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Preview
H.R. Pourreza
Preview
H.R. Pourreza
Preview
 Color image processing is divide into
two major area:
 Full-Color Processing
 Pseudo-Color Processing
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Color Fundamentals
The experiment of Sir Isaac Newton, in 1666.
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Color Fundamentals (con’t)c
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Color Fundamentals (con’t)c
 Basic quantities to describe the quality of light
source:
 Radiance: Total amount of energy that flows from
the light source (in W).
 Luminance: A measure of the amount of energy
an observer perceives from the light source (in lm)
 Brightness: A subjective descriptor that embodies
the achromatic notion of intensity and is practical
impossible to measure.
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Color Fundamentals (con’t)
Standard wavelength values for the
primary colors
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Color Fundamentals (con’t)
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Color Fundamentals (con’t)
 The characteristics generally used to distinguish
one color from another are Brightness, Hue, and
Saturation.
 Hue: Represents dominant color as perceive by an
observer.
 Saturation: Relative purity or the amount of white
light mixed with a hue
 Hue and saturation taken together are called
Chromaticity, and therefore, a color may be
characterized by its Brightness and Chromaticity.
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Color Fundamentals (con’t)
 Tri-stimulus values: The amount of Red, Green and
Blue needed to form any particular color
Denoted by: X, Y and Z
 Tri-chromatic coefficient:
X
x
X Y  Z
Y
y
X Y  Z
x  y  z 1
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Z
z
X Y  Z
Color Fundamentals (con’t)
Chromaticity Diagram
Green Point =
62% green,
25% red,
13% blue.
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Color Fundamentals (con’t)
Color Gamut
produced by RGB
monitors
Color Gamut
produced by high
quality color printing
device
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Color Models
 The purpose of a color model (also called
color space or color system) is to facilitate the
specification of colors in some standard,
generally accept way.
 RGB (red,green,blue) : monitor, video camera.
 CMY(cyan,magenta,yellow),CMYK (CMY, black)
model for color printing.
 and HSI model,which corresponds closely with the
way humans describe and interpret color.
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The RGB Color Models
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The RGB Color Models (con’t)
2 
8 3
 16,777,216 Colors
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The RGB Color Models (con’t)
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The RGB Color Models (con’t)
Safe RGB Colors (Safe Web colors)
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The RGB Color Models (con’t)
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The CMY and CMYK Color Models
 Cyan, Magenta and Yellow are the secondary colors
of light
 Most devices that deposit colored pigments on
paper, such as color printers and copiers, require CMY
data input.
 C  1  R 
 M   1  G 
    
 Y  1  B 
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The HSI Color Models
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The HSI Color Models
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The HSI Color Models
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The HSI Color Models
 Converting colors from RGB to HSI

H 
360 
if B  G
if B  G
1


[(
R

G
)

(
R

B
)]

1 
2
  cos 
2
1/ 2 
[(
R

G
)

(
R

B
)(
G

B
)]




3
S  1
[min(R, G, B)]
( R  G  B)
1
I  ( R  G  B)
3
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The HSI Color Models
 Converting colors from
HIS to RGB

RG sector :
0  H  120
B  I (1  S )

S cos H 
R  I 1 


cos(
60

H
)


G  3I  ( R  B)
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The HSI Color Models
 Converting colors from
HIS to RGB

GB sector : 120  H  240
H  H  120
R  I (1  S )

S cos H 
G  I 1 


cos(
60

H
)


B  3I  ( R  G)
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The HSI Color Models
 Converting colors from
HIS to RGB

BR sector :
240  H  360
H  H  240
G  I (1  S )

S cos H 
B  I 1 


cos(
60

H
)


R  3I  (G  B)
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The HSI Color Models
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The HSI Color Models
RGB
H
H
S
S
I
I
RGB
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Pseudocolor Image Processing
 Pseudocolor (also called false color) image
processing consists of assigning colors to gray
values based on a specified criterion.
 The principal use of pseudocolor is for human
visualization and interpretation of gray-scale
events in an image or sequence of images.
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Intensity Slicing
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Intensity Slicing (con’t)
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Intensity Slicing (con’t)
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Intensity Slicing (con’t)
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Gray Level to Color Transformations
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Gray Level to Color Transformations
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Gray Level to Color Transformations
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Gray Level to Color Transformations
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Gray Level to Color Transformations
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Gray Level to Color Transformations
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Basic of Full Color Image Processing
Let c represent an arbitrary vector in RGB color space
cR   R 
c  cG   G 
cB   B 
For an image of size M*N,
 c R ( x, y )   R ( x, y ) 
c( x, y )  cG ( x, y )  G ( x, y )
cB ( x, y )   B ( x, y ) 
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Basic of Full Color Image Processing
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Basic of Full-Color Image Processing
 Major categories of full-color Image
processing:
 Per-color-component processing
 Vector-based processing
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Basic of Full-Color Image Processing
Color Transformation
Processing the components of a color image
within the context of a single color model.
g ( x, y)  T  f ( x, y)
si  Ti r1 , r2 ,, rn ,
Color components of g
i  1,2,...,n
Color components of f
Color mapping functions
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Full-Color Image Processing
Color Transformation
CMYK
RGB
 Some difficulty in interpreting the
HUE:
 Discontinuity where 0 and
360º meet.
HSI
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 Hue is undefined for a
saturation 0
Full-Color Image Processing
Color Transformation: Modify the Intensity
g ( x, y)  kf ( x, y)
si  kri
s1  r1
i  1,2,3
si  kri  (1  k )
i  1,2,3
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s2  r2
s3  kr3
Full-Color Image Processing
Color Transformation: Color Complement
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Full-Color Image Processing
Color Transformation: Color Complement
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Full-Color Image Processing
Color Transformation: Color Slicing
Motive: Highlighting a specific range of colors in an
image
Basic Idea:
 Display the color of interest so that they stand out
from background
 Use the region defined by the colors as a mask for
further processing

0.5
si  
r
i
W

if  rj  a j  
2  any1 j n ,

otherwise
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i  1,2,...,n
Full-Color Image Processing
Color Transformation: Color Slicing
1. Colors of interest are enclosed by cube (or hypercube
for n>3)

0.5
si  
r
i
W

if  rj  a j  
2  any1 j n ,

otherwise
i  1,2,...,n
2. Colors of interest are enclosed by Sphere
n

2
2
0.5 if  (rj  a j )  R0
si  
,
j 1

otherwise
ri
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i  1,2,...,n
Full-Color Image Processing
Color Transformation: Color Slicing
Cube
Sphere
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Full-Color Image Processing
Color Transformation: Tone and Color Correction
The tonal rang of an image, also called its key-type,
refers to its general distribution of color intensities.
 High-key images: Most of the information is
concentrated at high intensities.
 Low-key images: Most of the information is
concentrated at low intensities.
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Full-Color Image Processing
Color Transformation: Tonal Correction
Middle-key Image
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Full-Color Image Processing
Color Transformation: Tonal Correction
High-key Image
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Full-Color Image Processing
Color Transformation: Tonal Correction
Low-key Image
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Full-Color Image Processing
Color Transformation: Color Correction
The proportion of any color can be increased by decreasing the
amount of the opposite (or complementary) color in the image or by
raising the proportion of the two immediately adjacent colors or
decreasing the percentage of the two colors adjacent to the
complement.
Magenta 
Removing Red
and Blue
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Adding Green
Full-Color Image Processing
Color Transformation: Color Correction
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Full-Color Image Processing
Color Transformation: Histogram Processing
Histogram
Equalizing the
Intensity
Saturation
Adjustment
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Full-Color Image Processing
Color Image Smoothing
Averaging:
1
c ( x, y ) 
K
 c( x, y )
( x , y )S xy
1

R ( x, y ) 


 K ( x , y )S xy

1

c ( x, y )  
G ( x, y ) 

 K ( x , y )S xy

1

 K  B ( x, y ) 
 ( x , y )S xy

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Full-Color Image Processing
Color Image Smoothing
Red
Blue
Green
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Full-Color Image Processing
Color Image Smoothing
Hue
Saturation
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Intensity
Full-Color Image Processing
Color Image Smoothing
Averaging R,G and B
Averaging Intensity
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Difference
Full-Color Image Processing
Color Image Sharpening
The Laplacian of Vector c :
  2 R ( x, y ) 
 2

2
 c( x, y )   G ( x, y )
  2 B ( x, y ) 


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Full-Color Image Processing
Color Image Sharpening
Sharpening R,G and B
Sharpening Intensity
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Difference
Full-Color Image Processing
Color Segmentation
Segmentation is a process that partitions an
image into regions
 Segmentation in HIS Color Space
 Segmentation in RGB Vector Space
 Color Edge Detection
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Full-Color Image Processing
Color Segmentation: in HIS Color Space
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Full-Color Image Processing
Color Segmentation: in RGB Vector Space
z is similar to a if the distance between them is less than a
specified threshold.
Euclidian Distance: D(z, a)  z  a

 ( z
 (z  a)T (z  a)
Generalized form:

1/ 2
 aR )  ( zG  aG )  ( z B  aB )
2
R

D(z, a)  (z  a)T C1 (z  a)
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2

1/ 2

2 1/ 2
Full-Color Image Processing
Color Segmentation: in RGB Vector Space
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Full-Color Image Processing
Color Segmentation: Color Edge Detection
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Full-Color Image Processing
Color Segmentation: Color Edge Detection
R
G
B
r
g
b
x
x
x
R
G
B
v
r
g
b
y
y
y
u
R
G
B
g xx  u u 


x
x
x
2
2
2
2
2
2
T
R
G
B
g yy  v v 


y
y
y
T
R R G G B B
g xy  u v 


x y x y x y
T
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Full-Color Image Processing
Color Segmentation: Color Edge Detection
 2 g xy 
1
1
  tan 

2
 g xx  g yy 


1/ 2
1





F ( )   g xx  g yy  g xx  g yy cos 2  2 g xy sin 2 
2

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Full-Color Image Processing
Color Segmentation: Color Edge Detection
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Full-Color Image Processing
Color Segmentation: Color Edge Detection
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Full-Color Image Processing
Noise in Color Images
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Full-Color Image Processing
Noise in Color Images
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Full-Color Image Processing
Noise in Color Images
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Full-Color Image Processing
Color Image Compression
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