Lect07.ppt digital image processing for computer and eng student

CS589-04 Digital Image Processing
CS589-04 Digital Image Processing
Lecture 6. Color Image
Lecture 6. Color Image
Processing
Processing
Spring 2008
Spring 2008
New Mexico Tech
New Mexico Tech

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Color Fundamentals
Color Fundamentals

11/07/25 3
Color Fundamentals
Color Fundamentals

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Color Fundamentals
Color Fundamentals
► 6 to 7 million cones in the human eye can be divided
into three principal sensing categories, corresponding
roughly to red, green, and blue.
65%: red 33%: green 2%: blue (blue cones are the
most sensitive)

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Color Fundamentals
Color Fundamentals

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Color Fundamentals
Color Fundamentals
► The characteristics generally used to distinguish one
color from another are brightness, hue, and saturation
brightness: the achromatic notion of intensity.
hue: dominant wavelength in a mixture of light waves,
represents dominant color as perceived by an observer.
saturation: relative purity or the amount of white light
mixed with its hue.

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Color Fundamentals
Color Fundamentals
► Tristimulus
Red, green, and blue are denoted X, Y, and Z,
respectively. A color is defined by its trichromatic
coefficients, defined as
X
x
X Y Z
Y
y
X Y Z
Z
z
X Y Z

 

 

 

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CIE Chromaticity Diagram
CIE Chromaticity Diagram
It shows color
composition
as a function
of x (red) and
y (green)

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RGB Color Model
RGB Color Model

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RGB Color Model
RGB Color Model
Pixel depth
The total number of
colors in a 24-bit RGB
image is (28
)3
=
16,777,216

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Safe RGB colors (or
safe Web colors) are
reproduced faithfully,
reasonably
independently of
viewer hardware
capabilities

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The CMY and CMYK Color Models
The CMY and CMYK Color Models
1
1
1
C R
M G
Y B
     
     
 
     
     
     
Equal amounts of the pigment primaries, cyan, magenta,
and yellow should produce black. In practice, combining
these colors for printing produces a muddy-looking black.
To produce true black, the predominant color in printing,
the fourth color, black, is added, giving rise to the CMYK
color model.

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http://en.wikipedia.org/wiki/CMYK
CMY vs. CMYK

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HIS Color Model
HIS Color Model
brightness: the achromatic notion of intensity.
hue: dominant wavelength in a mixture of light waves, represents dominant
color as perceived by an observer.
saturation: relative purity or the amount of white light mixed with its hue.

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HIS Color Model
HIS Color Model

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HIS Color Model
HIS Color Model

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HIS Color Model
HIS Color Model

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Converting Colors from RGB to HSI
► Given an image in RGB color format, the H component
of each RGB pixel is obtained using the equation
if B G
360 if B>G
H







 
 
1
1/2
2
1
( ) ( )
2
cos
( )( )
R G R B
R G R B G B
 
 
  
 
  
 
 
   
 
 

11/07/25 22
► Given an image in RGB color format, the saturation
component is given by
 
3
1 min( , , )
( )
S R G B
R G B
 
 

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► Given an image in RGB color format, the intensity
component is given by
 
1
3
I R G B
  

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Converting Colors from HSI to RGB
► RG sector
(1 )
cos
1
cos(60 )
and
3 ( )
B I S
S H
R I
H
G I R B
 
 
 
 

 
  

(0 120 )
H
 
 

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► RG sector
120
(1 )
cos
1
cos(60 )
and
3 ( )
H H
R I S
S H
G I
H
B I R G
 
 
 
 
 

 
  


(120 240 )
H
 
 

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► RG sector
240
(1 )
cos
1
cos(60 )
and
3 ( )
H H
G I S
S H
B I
H
R I G B
 
 
 
 
 

 
  


(240 360 )
H
 
 

11/07/25 30
Pseudocolor Image Processing
► The process of assigning colors to gray values based on
a specified criterion.
► Intensity Slicing
( , ) if ( , )
k k
f x y c f x y V
 

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► Intensity to Color Transformation

11/07/25 37
The images are obtained
from an airport X-ray
scanning system.
The left contains ordinary
articles and the right
contains the same articles
as well as a block of
simulated plastic
explosives.

11/07/25 41
Pseudocolor by combining
several of the sensor
images from the Galileo
spacecraft, some of which
are in spectral regions not
visible to the eye.
Bright red depicts
materials newly ejected
from an active volcano on
Io, and the surrounding
yellow materials are older
sulfur deposits.

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Basics of Full-Color Image Processing
Let represent an arbitrary vector in RGB color space:
At coordinates ( , ),
( , ) ( , )
( , ) ( , ) ( , )
( , ) ( , )
R
G
B
R
G
B
c
c R
c c G
c B
x y
c x y R x y
c x y c x y G x y
c x y B x y
   
   
 
   
   
   
   
   
 
   
   
   

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11/07/25 44
Color Transformations
Color Transformations
 
( , ) ( , )
g x y T f x y

1 2
( , ,..., ), 1,2,..., .
i i n
s T r r r i n
 

11/07/25 46
( , ) ( , )
g x y kf x y

,
1,2,3.
i i
s kr
i


(1 ),
1,2,3.
i i
s kr k
i
  

3 3
s kr


11/07/25 49
Color slicing
Color slicing
► Highlighting a specific range of colors in an image
1 2
If the colors of interest are enclosed by a cube of width W
and centered at a protypical color with components
( , ,..., ), the necessary set of transformations is
0.5 if
n
i
a a a
s  any 1
| | / 2
otherwise
j j j n
i
r a W
r
 
  
 
  




11/07/25 50
Color slicing
Color slicing
 
0
2 2
0
1
If a sphere is used to specify the colors of interest,
R is the radius of the enclosing of its center.
The transformations is
0.5 if
n
j j
j
i
i
r a R
s
r

 


otherwise






11/07/25 51
Color slicing
Color slicing

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Tone and
Tone and
Color
Color
Corrections
Corrections

11/07/25 55
Color Image Smoothing
Color Image Smoothing
( , )
Let denote the set of coordinates defining a neighborhood
centered at ( , ) in an RGB color image. The average of the
RGB component vectors in this neighborhood is
1
( , ) ( , )
xy
xy
s t S
S
x y
c x y c s t
K 

( , )
( , )
( , )
1
( , )
1
( , )
1
( , )
xy
xy
xy
s t S
s t S
s t S
R s t
K
G s t
K
B s t
K



 
 
 
 
 

 
 
 
 
 

 


11/07/25 59
Color Image Sharpening
Color Image Sharpening
 
2
2 2
2
The Laplacian of vector c is
( , )
( , ) ( , )
( , )
R x y
c x y G x y
B x y
 

 
  
 
 

 

11/07/25 61
Image
Image
Segmentation
Segmentation
Based on
Based on
Color:
Color:
Segmentation
Segmentation
in HIS Color
in HIS Color
Space
Space

11/07/25 62
Segmentation in RGB Vector Space
Segmentation in RGB Vector Space
1/2
1/2
2 2 2
Let the average color of interest is denoted by the
RGB vector . Let denote an arbitrary point in
RGB space.
( , ) ( ) ( )
( ) ( ) ( )
T
R R G G B B
a z
D z a z a z a z a
z a z a z a
 
    
 
 
     
 

11/07/25 64
Color Edge Detection (1)
Let r, g, and b be unit vectors along the R, G, and B
axis of RGB color space, and define vectors
u r g b
and
v r g b
R G B
x x x
R G B
y y y
  
  
  
  
  
  

11/07/25 65
2 2 2
2 2 2
u u=
v v=
and
u v=
xx
yy
xy
R G B
g
x x x
R G B
g
y y y
R R G G B B
g
x y x y x y
  
  
  
  
  
  
     
  
     




11/07/25 66
1
The direction of maximum rate of change of c( , ) is given by
the angle
2
1
( , ) tan
2
The value of the rate of change at ( , ) in the direction of ( , ),
is given by
1
F ( , )=
2
xy
xx yy
x
x y
g
x y
g g
x y x y
x y g




 
  

 
 
   
1/2
cos2 ( , ) 2 sin 2 ( , )
x yy xx yy xy
g g g x y g x y
 
 
 
   
 
 
 

Lect07.ppt digital image processing for computer and eng student

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