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# Image Processing - Session 1

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## Dalia Eissa

on 8 July 2014

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#### Transcript of Image Processing - Session 1

Image Processing
Why?!
Computer Vision
VS
Image Processing
Focus on syntax and low-level architectures.
Primitive operations such as to reduce noise, contrast enhancement and image sharpening.

Research area within Electrical engineering/ Signal processing.

Both inputs and outputs are images.
Computer
Vision
Segmentation, descriptions of partitioned objects and classification of individual objects.

Research area within Computer science/ Artificial intelligence.

Inputs are generally images,
But its outputs are
attributes, measurements or description
extracted from those images.
Image
Processing
Visualization
- Observe the objects that are not visible by human eye.
Image sharpening and restoration
-create a better image.

Image retrieval
-seek for the image of interest
X-ray mammogram
Measurement of pattern
-measures various objects in an image
Image Recognition
-distinguish the objects in an image
What is a Digital Image?
discrete samples f [x,y] representing continuous image f (x,y)
Intensity Transformation and Spatial Filtering

Image Domains

spatial

frequency
Spatial means place so spatial domain is simply the plane which describes the place of a pixel.
it is usually drawn like x,y coordinates

Spatial domain OPs

The general expression of the spatial operations is
g(x , y)=T[f(x , y)]
Where
f(x , y) is the input image
g(x , y) is the output image
T is the operator

the most important operation is the intensity transformation
s=T(r)
Where
s is the intensity of single pixel in output image
r is the intensity of single pixel in input image
T is now the intensity transformation operator

Intensity Transformation Functions

The basic intensity transformation functions are as follows:
Identity
Negative
Logarithmic (log and anti log)
Power law

Matlab Function
g = imadjust (f, [low_in high_in], [low_out high_out], gamma)
(low_in, high_in, low_out, high_out). are values between 0 and 1 and are proportional to the minimum and maximum value of the pixel, i.e. [0 255] in case of 8 bit
(F) Is the input image variable
(gamma). Is a given parameter discussed later

Identity
It is a trivial function where it just outputs the same value of the pixel
Example: pixel p(40,100) of intensity value r=100 when the operator T which is the identity is operated on this pixel the output value of the same pixel is s=100 also

Negative
Say we have a pixels of range of intensities of [0 L-1] the negative of this values is obtained by
s=L-1-r

Negative
Log transform
The general form is
s=clog(1+r)
Where
c is some const. And 1+r is just to avoid case of r=0
This function is used to expand the pixels in the dark region and compressing light regions. it is used when we are interested in dark areas

Log transform
Power law (gamma)
The general form is
s=c(r)^gamma
Where (gamma) and c are const.
This function operates as the log function in case of gamma is of fraction value but with wide applications
if c=gamma=1 that is identity!
Increasing gamma will be like using inverse log

gamma=2
gamma=0.5
Image Histogram
What Is Contrast ??
Low Contrast Images
High Contrast Images
Histogram Stretching