1. Fundamentals of Image Processing A Seminar on By Alok K. Watve
20 November 2018

2. Applications of image processing
Gamma ray imaging
X-ray imaging
Multimedia systems
Satellite imagery
Flaw detection and quality control
And many more…….
20 November 2018

3. Fundamental Steps in digital image processing
Image acquisition
Image enhancement(gray or color images)
Wavelet and multi-resolution processing
Compression
Morphological processing
Segmentation
Representation & description
Object recognition
Low level processing
Medium level processing
High level processing
20 November 2018

4. Image enhancement in spatial domain
Binary images
Only two colors
Gray images
A range of colors(not more than 256) from black to white
Color images
Contain several colors(as many as 224)
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5. Image : A 2D function represented by I = f(x,y)
Definitions
Image : A 2D function represented by
I = f(x,y)
where I = intensity of the point(x,y)
Foreground : Objects of interest in an image
Background : Everything that’s not in foreground
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6. freq(I) = # pixels with intensity I
Definitions
Histogram : A graph of frequency(of an intensity) versus intensity. Frequency is expressed as count of pixels
freq(I) = # pixels with intensity I
Spatial resolution : Smallest discernible detail in the image. Depends on the sampling.
Gray-level resolution : smallest discernible change in the gray level change.
20 November 2018

7. Basic gray level transformations
Image negatives
s = (L – 1) – r
Where,
s = output intensity*
r = input intensity*
(L – 1) = Maximum intensity*
*These notations will be used throughout the seminar
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8. Basic gray level transformations
Original image Negative image
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All images: courtesy :

9. Basic gray level transformations
Power law transformation
s = c. rγ
Here,
c is a constant
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10. Basic gray level transformations
Original image Transformed image (c = 1, γ = 0.3)
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11. Basic gray level transformations
Contrast stretching : increases dynamic range
L-1
S2 Output intensity S1
0, L L L-1
Input intensity
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12. Basic gray level transformations
Original image Image obtained by contrast stretching
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13. Basic gray level transformations
Bit plane slicing
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14. Basic gray level transformations
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15. 20 November 2018

16. Basic gray level transformations
Histogram equalization
Image of mars’ moon histogram
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17. Basic gray level transformations
Histogram equalization transformation can be expressed as a monotonically increasing function with domain and range = [0, 1]**
Assuming the intensities are normalized in the range [0,1]
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18. Basic gray level transformations
A low contrast image and its histogram
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19. Basic gray level transformations
Result of histogram equalization
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20. Filtering in spatial domain
Concept of frequency
Modeling filters using convolution in spatial domain
Implementing filters using masks
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21. g(x, y) = Σs Σt w(s, t).f(x+s,y+t)
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22. Filtering in spatial domain
w(-1,-1) w(-1, 0) w(-1, 1)
w(0,-1) w(0, 0) w(0, 1)
f(x-1,y-1) f(x-1, y) f(x-1, y+1)
w(1,-1) w(1, 0) w(1, 1)
f(x,y-1) f(x, y) f(x, y+1)
f(x+1,y-1) f(x+1, y) f(x+1, y+1)
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23. Averaging filter Weighted average filter
Low pass filter
Averaging filter Weighted average filter
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24. 20 November 2018

25. A noisy image Filtered image
Median filter
A noisy image Filtered image
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26. Computing gradients in spatial domain Laplacian filter
High pass filter
Computing gradients in spatial domain
Laplacian filter
Other masks (operators)
Roberts
Sobel
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27. Designing high pass filters Method 1 g(x, y) = f(x, y) + fhp(x, y)
g(x, y) = f(x, y) – flp(x, y)
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28. Laplacian operators 0 -1 0 -1 -1 -1 -1 5 -1 -1 9 -1 0 -1 0 -1 -1 -1
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29. Sobel Operators -1 -2 -1 -1 0 1 0 0 0 -2 0 2 1 2 1 -1 0 1
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30. Roberts cross gradient operators
1 0
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31. High pass filter
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32. To be contd….
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33. Digital image processing, second edition - R. C. Gonzalez, R. E. Woods
References
Digital image processing, second edition - R. C. Gonzalez, R. E. Woods
Fundamental of digital image processing – A. K. Jain
20 November 2018