1. What is Digital Image Processing?
Application Area:
Improvement of Pictorial Information for Human Interpretation
Processing of Image Data for Storage, Transmission and Representation for Autonomous Machine Perception

2. What is Digital Image Processing?
An Image:
g(x , y)
Discretization
g(i , j)
f(i , j) Digital Image
Quantization
f(i0 , j0) : Picture Element, Image Element, Pel, Pixel

3. What is Digital Image Processing?
DIP Definition:
A Discipline in Which Both the Input and Output of a Process are Images.
Process
Image

4. What is Digital Image Processing?
Image Analysis
Image Processing
Vision
Low-Level Process
Mid-Level Process
High-Level Process
Reduce Noise
Contrast Enhancement
Image Sharpening
Segmentation
Classification
Making Sense of an Ensemble of Recognized Objects

5. The Origins of Digital Image Processig

6. The Origins of Digital Image Processig

7. The Origins of Digital Image Processig

8. Fields that Use Digital Image Processing

9. Gamma-Ray Imaging

10. X-Ray Imaging

11. Imaging in the Ultraviolet Band

12. Imaging in the Visible And IR Band

13. Imaging in the Visible And IR Band

14. Imaging in the Visible And IR Band

15. Imaging in the Visible And IR Band

16. Imaging in the Visible And IR Band

17. Imaging in the Visible And IR Band

18. Imaging in the Visible And IR Band

19. Imaging in the Visible And IR Band

20. Imaging in the Microwave Band

21. Imaging in the Radio Band

22. Other Imaging Modalities

23. Other Imaging Modalities

24. Other Imaging Modalities

25. Other Imaging Modalities

26. Fundamental Steps in Digital Image Proc.

27. Components of an Image Proc. System

28. Image Sensing and Acquisition
Transform of illumination energy into digital images:
The incoming energy is transformed into a voltage by the combination of input electrical power and sensor material.
Output voltage waveform = response of the sensor(s)
A digital quantity is obtained from each sensor by digitizing its response.

29. Image acquisition using a single sensor
Image acquisition using sensor strips

31. Sampling and Quantization
Representing Digital Images

32. Basic Relationships Between Pixels
• Neighborhood
• Adjacency
• Connectivity
• Paths
• Regions and boundaries
Neighbors of a Pixel
• Any pixel p(x, y) has two vertical and two horizontal neighbors, given by
(x+1, y), (x-1, y), (x, y+1), (x, y-1)
• This set of pixels are called the 4-neighbors of P, and is denoted by N4(P).
• Each of them are at a unit distance from P.
The four diagonal neighbors of p(x,y) are given by,
(x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1 ,y-1)
• This set is denoted by ND(P).
• Each of them are at Euclidean distance of
1.414 from P.
8-neighbors of a pixel p are its vertical horizontal and 4 diagonal neighbors denoted by N8(p)

33. Adjacency Let V be set of gray levels values used to define adjacency.
• 4-adjacency: Two pixels p and q with values from V are 4- adjacent if q is in the set N4(p).
• 8-adjacency: Two pixels p and q with values from V are 8-adjacent if q is in the set N8(p).
• m-adjacency: Two pixels p and q with values from V are madjacent
if,
– q is in N4(P).
– q is in ND(p) and the set [ ] is empty
(has no pixels whose values are from V).

34. Connectivity : Paths & Path lengths Here n is the length of the path.
4-connected, if q is in the set N4(p)
b. 8-connected, if q is in the set N8(p)
c. m-connected, iff
i. q is in N4(p) or
ii. q is in ND(p) and the set is empty
Paths & Path lengths
A path from pixel p with coordinates (x, y) to pixel q with coordinates (s, t) is a sequence of distinct pixels with
coordinates: (x0, y0), (x1, y1), (x2, y2) … (xn, yn),
Here n is the length of the path.

35. Distance measures
Given pixels p, q and z with coordinates (x, y), (s, t), (u, v) respectively, the distance function D has following properties:
a. D(p, q) ≥0 , [D(p, q) = 0, iff p = q]
b. D(p, q) = D(q, p)
c. D(p, z) ≤D(p, q) + D(q, z)
The following are the different Distance measures:
• Euclidean Distance :
De(p, q) = [(x-s)2 + (y-t)2]
b. City Block Distance:
D4(p, q) = |x-s| + |y-t|
c. Chess Board Distance:
D8(p, q) = max(|x-s|, |y-t|)