1. Digital Image Processing
Chapter 2: Digital Image Fundamentals

2. Elements of Visual Perception
Structure of the human eye

3. Rods and cones in the retina

4. Image formation in the eye

5. Brightness adaptation and discrimination

6. Brightness discrimination

7. Weber ratio

8. Perceived brightness

9. Simultaneous contrast

10. Optical illusion

11. Light and the Electromagnetic Spectrum

12. Wavelength

13. Image Sensing and Acquisition

14. Image acquisition using a single sensor

15. Using sensor strips

16. A simple image formation model

17. Illumination and reflectance
Illumination and transmissivity

18. Image Sampling and Quantization

19. Sampling and quantization

20. Representing digital images

21. Number of storage bits

22. Spatial and gray-level resolution

23. Subsampled and resampled

24. Varying the number of gray levels

25. Varying the number of gray levels

26. N and k in different-details images

27. Isopreference

28. Moire pattern

29. Zooming and shrinking

30. Some Basic Relationships Between Pixels
Neighbors of a pixel
: 4-neighbors of p
, , ,
: four diagonal neighbors of p
, , ,
: 8-neighbors of p
and

31. Adjacency : The set of gray-level values used to define adjacency
4-adjacency: Two pixels p and q with values from V are 4-adjacency if q is in the set
8-adjacency: Two pixels p and q with values from V are 8-adjacency if q is in the set

32. m-adjacency (mixed adjacency): Two pixels p and q with values from V are m-adjacency if
q is in , or
q is in and the set has no pixels whose values are from V

33. Subset adjacency
S1 and S2 are adjacent if some pixel in S1 is adjacent to some pixel in S2
Path
A path from p with coordinates to pixel q with coordinates is a sequence of distinct pixels with coordinates
, ,…,
where = , = , and pixels and are adjacent

34. Region
We call R a region of the image if R is a connected set
Boundary
The boundary of a region R is the set of pixels in the region that have one or more neighbors that are not in R
Edge
Pixels with derivative values that exceed a preset threshold

35. Distance measures Euclidean distance City-block distance
Chessboard distance

36. Linear operation distance: The shortest m-path between the points
H is said to be a linear operator if, for any two images f and g and any two scalars a and b,

37. Example Zooming and Shrinking Images by Pixel Replication
(a) Write a computer program capable of zooming and shrinking an image by pixel replication. Assume that the desired zoom/shrink factors are integers. You may ignore aliasing effects. You will need to download Fig. 2.19(a).
(b) Download Fig (a) and use your program to shrink the image from 1024 x 1024 to 256 x 256 pixels.
(c) Use your program to zoom the image in (b) back to 1024 x Explain the reasons for their differences.

38.
Fig2.19(a).bmp
subsample.c
resample.c