1. CIS 601 Image Fundamentals
Dr. Rolf Lakaemper

2. Parts of these slides base on the textbook Digital Image Processing
Fundamentals
Parts of these slides base on the textbook
Digital Image Processing
by Gonzales/Woods
Chapters 1 / 2

3. basic concepts about digital images
Fundamentals
These slides show
basic concepts about digital images

4. Let’s have a look at the human eye
Fundamentals
Let’s have a look at the human eye

5. Fundamentals

6. consists of cones and rods
Fundamentals
We are mostly interested in the retina:
consists of cones and rods
Cones
color receptors
About 7 million, primarily in the retina’s central portion
for image details
Rods
Sensitive to illumination, not involved in color vision
About 130 million, all over the retina
General, overall view

7. Maps our 3D reality to a 2 dimensional image !
Fundamentals
The human eye
Is able to perceive electromagnetic waves in a certain spectrum
Is able to distinguish between wavelengths in this spectrum (colors)
Has a higher density of receptors in the center
Maps our 3D reality to a 2 dimensional image !

8. maps our continous (?) reality to a (spatially) DISCRETE 2D image
Fundamentals
…or more precise:
maps our continous (?) reality to a (spatially) DISCRETE 2D image

9. Some topics we have to deal with: Sharpness Brightness
Fundamentals
Some topics we have to deal with:
Sharpness
Brightness
Processing of perceived visual information

10. The eye is able to deal with sharpness in different distances
Fundamentals
Sharpness
The eye is able to deal with sharpness in different distances

11. The eye is able to adapt to different ranges of brightness
Fundamentals
Brightness
The eye is able to adapt to different ranges of brightness

12. Processing of perceived information: optical illusions
Fundamentals
Processing of perceived information: optical illusions

13. Fundamentals
optical illusions:
Digital Image Processing does NOT (primarily) deal with cognitive aspects of the perceived image !

14. Fundamentals
What is an image ?

15. Fundamentals
The retinal model is mathematically hard to handle (e.g. neighborhood ?)

16. Easier: 2D array of cells, modelling the cones/rods
Fundamentals
Easier: 2D array of cells, modelling the cones/rods
Each cell contains a numerical value (e.g. between 0-255)

17. The position of each cell defines the position of the receptor
Fundamentals
The position of each cell defines the position of the receptor
The numerical value of the cell represents the illumination received by the receptor 5 7 1 12 4 … … …

18. With this model, we can create GRAYVALUE images
Fundamentals
With this model, we can create GRAYVALUE images
Value = 0: BLACK (no illumination / energy)
Value = 255: White (max. illumination / energy)

19. A 2D grayvalue - image is a 2D -> 1D function,
Fundamentals
A 2D grayvalue - image is a 2D -> 1D function,
v = f(x,y)

20. As we have a function, we can apply operators to this function, e.g.
Fundamentals
As we have a function, we can apply operators to this function, e.g.
H(f(x,y)) = f(x,y) / 2
Operator
Image (= function !)

21. H(f(x,y)) = f(x,y) / 2 Fundamentals 6 8 2 3 4 1 12 200 20 10 6 100 103 4 1 12
200 20 10 6
100 10 5

22. Remember: the value of the cells is the illumination (or brightness)
Fundamentals
Remember: the value of the cells is the illumination (or brightness) 6 8 2 3 4 1 12
200 20 10 6
100 10 5

23. Image Acquisition and Representation
Fundamentals
The mandatory steps:
Image Acquisition and Representation

24. Fundamentals
Acquisition

25. Fundamentals
Acquisition

26. Typical sensor for images: CCD Array (Charge Couple Devices)
Fundamentals
Typical sensor for images:
CCD Array (Charge Couple Devices)
Use in digital cameras
Typical resolution 1024 x 768 (webcam)

27. Fundamentals
CCD

28. Fundamentals
CCD

29. Fundamentals
CCD (3.2 million pixels)

30. Fundamentals
Representation
The Braun Tube

31. Fundamentals
Representation
Black/White and Color

32. Color Representation: Red / Green / Blue
Fundamentals
Color Representation: Red / Green / Blue
Model for
Color-tube
Note: RGB is not the
ONLY color-model, in fact
its use is quiet restricted.
More about that later.

33. Color images can be represented by
Fundamentals
Color images can be represented by
3D Arrays (e.g. 320 x 240 x 3)

34. But for the time being we’ll handle 2D grayvalue images
Fundamentals
But for the time being we’ll handle 2D grayvalue images

35. Digital vs. Analogue Images
Fundamentals
Digital vs. Analogue Images
Analogue:
Function v = f(x,y): v,x,y are REAL
Digital:
Function v = f(x,y): v,x,y are INTEGER

36. Stepping down from REALity to INTEGER coordinates x,y: Sampling
Fundamentals
Stepping down from REALity to INTEGER coordinates x,y: Sampling

37. Stepping down from REALity to INTEGER grayvalues v : Quantization
Fundamentals
Stepping down from REALity to INTEGER grayvalues v : Quantization

38. Sampling and Quantization
Fundamentals
Sampling
and
Quantization

39. MATLAB demonstrations of sampling and quantization effects
Fundamentals
MATLAB demonstrations of sampling and quantization effects