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Lecture 2 Point Operations
Lecture 2
Point Operations
Last lecture covered
� Images and pictures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . � What is image processing? . . . . . . . . . . . . . . . . . . . . . . . .� Image Acquisition and sampling . . . . . . . . . . . . . . . . . . . � Images and digital images . . . . . . . . . . . . . . . . . . . . . . . .� Some applications . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . � Aspects of image processing . . . . . . . . . .. . . . . . . . . . . . . � An image processing task . . . . . . . . . . . . . . . . . . . . . . . . .� Types of digital images . . . . . . . . . . . . . . . . . . . . . . . . . . . � Image File Sizes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . � Spatial Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . � Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Content
� Point Processing
2.1 Introduction . . . . . . . . . . . . . . . . . . . .
2.2 Arithmetic operations . . . . . . . . . . . .
2.3 Histograms . . . . . . . . . . . . . . . . . . . .
2.4 Lookup tables . . . . . . . . . . . . . . . . . .
� Exercises
Pixel neighbors
Neighborhood processing vs. Point
operations
�� Neighborhood processing. Neighborhood processing.
� To change the grey level of a given pixel we
need only know the value of the grey levels
in a small neighborhood of pixels around the
given pixel.
�� Point operations. Point operations.
� A pixel's grey value is changed without any
knowledge of its surrounds.
What is the aim of “Point Operations”?
� Although point operations are the simplest, they contain some of the most powerful and widely used of all image processing operations.
� They are especially useful in image pre-processing, where an image is required to be modified before the main job is attempted.
2.2 Arithmetic operations
� These operations act by applying a simple function
y = f(x)
� Types of operations:
1. Addition/Subtraction
2. Multiplications/Division
3. Complementary
1. Addition/Subtraction
� To add /subtract a constant from each pixel in the image.
y = x ± C
0…………………..….255
Old image (x)
ne
w im
ag
e f
(x)
0…
……
……
……
….2
55
0…………………..….255
Old image (x)
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w im
ag
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(x)
0…
……
……
……
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55
C
y = f(x)
1. Addition/Subtraction
� Original (x) y = x + 128 y = x -128
� To multiply /divide each pixel in the
image x in/by a constant c.
y = x × C , y = x ÷ C
2. Multiplications/Division
0…………………..….255
Old image (x)
ne
w im
ag
e f
(x)
0…
……
……
……
….2
55
0…………………..….255
Old image (x)
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w im
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e f
(x)
0…
……
……
……
….2
55
�
y = f(x)
2. Multiplications/Division
3. Complements
� The complement of a greyscale image is its photographic negative.
What is image Solarization?
� Complementing only part of the image.
Color Images Operations
Size:
m×n×3 m×n m×n m×n
Matrix:
x x( : , : ,1) x( : , : ,2) x( : , : ,3)
Color Images Operations
� Possible channel operations:
� Changing the image lighting color.
� Swapping image channels.
� Eliminating color channels.
Changing the image lighting color
� To change the intensity of one or more channel by adding or subtracting a constant.
� R + C => the image will be reddish
� G + C => the image will be greenish
� B + C => the image will be bluish
� R,G + C => the image will be yellowish
� ….
RGB Cube
Changing the image lighting color
Swapping image channels
� To exchange the intensity between the image channels.
� R , G => the image will be GRB
� G , B => the image will be RBG
� B , R => the image will be BGR
� Other spaces could be obtained by swapping 2 channels at once like; GBR and BRG
Swapping image channels
R, G G,B R,B
Eliminating color channels
� To set one or more color channels by zero values.
� Eliminating R => image will be in cyan
� Eliminating G => image will be in magenta
� Eliminating B => image will be in yellow
� Eliminating R,G => image will be in blue
� Eliminating G,B => image will be in red
� Eliminating B,R => image will be in green
Eliminating color channels
R=0 G=0 B=0
G,B=0 R,B=0 R,G=0
Multiple Image Operations
� One can perform operations between two images or more for:
� Enhance the image visual appeal.
� Getting specific part of image between
similar images.(eg. motion tracking)
� For some preprocessing purposes.
Multiple Image Operations
� Assume two images x1 and x2: the possible operations between x1 and x2:
• y = x1 + x2
• y = x1 – x2• y = x1 × x2
• y = x1 ÷ x2
• y = diff(x1, x2)• y = max(x1,x2)• y = min(x1,x2)• y = avg(x1,x2)
• y = x1 AND x2• y = x1 OR x2• y = x1 XOR x2
Example
Addition
AND
Subtraction
OR
Exercise
1. Find the relation between the multiplication factor CC and the rotation
angle өө.
2. What do you expect if we perform the following function:
1. Y = x - R
