This document outlines the course contents for a class on digital image processing and machine vision. The 10-week course covers topics such as image acquisition, enhancement, segmentation, feature extraction, and advanced research areas. It includes 1-3 lectures per topic. Reference materials listed are books and journals in the field. The introduction defines an image and digital image processing. It provides examples of image processing applications in medical diagnosis, industrial uses, security, biometrics, and more. Key components of machine vision systems and a comparison of human and machine vision are also summarized.

1. Digital Image Processing
& Machine Vision
Instructed by
Dr. Abdul Rehman Abbasi
One picture is worth more than ten
thousand words

2. Course Contents
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
(with estimated no. of lectures)
Introduction & Motivation (1)
Fundamental Concepts (1)
Image Acquisition (1)
Image Enhancement (2)
Morphological Operations (1)
Image Segmentation (3)
Feature Extraction (3)
Hardware & Software Methods in Image Processing (1)
Advanced Research Areas in Image Processing (1)
Mini-Project Presentation or Research Article Presentations (2)

3. Reference Books & Journals
•
Digital Image Processing
by Rafael C. Gonzalez & Richard E. Woods (2nd Edition), Pearson Education
•
Digital Image Processing: A Practical Introduction using Java TM
by Nick Efford, Pearson Education
•
Applied Image Processing
by G.W. Awcock & R. Thomas , McGrawHill
•
Real-Time Image and Video Processing: From Research to Reality
Nasser Kehtarnavaz and Mark Gamadia, Morgan & Claypool Publishers
•
•
•
•
•
Image & Vision Computing, Journal (IVC)
Computer Vision & Image Understanding, Journal (CVIU)
International Journal of Computer Vision (IJCV)
IEEE Transactions on Pattern Analysis & Machine Intelligence (PAMI)
IEEE Transactions on Image Processing

4. Introduction to Image Processing
• An image is a 2-Dimensional function f(x,y) where x and y are
spatial coordinates, and amplitude f at any pair of coordinates
(x,y) is called the intensity or gray level of the image at that
point.
• When x,y, and f are finite and discrete we call it a digital image.
• Digital image processing means processing/computing digital
images using computational means such as using a digital
computer.

5. Image Function & Spatial Coordinates

6. Image Processing & the World

7. Motivation
 Medical Diagnosis
 Industrial Applications
 Security Applications
 Biometrics & Finance
 Seismic Analysis
 Aerial Applications
 Space Explorations

8. Medical Diagnosis
Digital Mammogram
MRI of Knee & Spine
Head CT Scan
Ultrasound

9. Industrial Applications
• Electronic Defect Detection
Product Testing/QA

10. Security Applications
Whole Body Scan
Vehicle Identification

11. Biometrics & Finance
Fingerprint Verification
Currency verification
Personnel Verification

12. Seismic Analysis
Mountains Ranges in Tibetan Plain
Seismic patterns showing oil
(natural resources) traps

13. Satellite Applications
Weather Forecast
Aerial Analysis

14. Space Explorations
Moon surface observation
North Pole observation

15. Imaging Spectrum
Images can work in a wide energy spectrum

16. Gamma Ray Imaging-1
• Nuclear Medicine
• Astronomical Observations

17. Gamma Ray Imaging-2
1.
2.
•
Inject a patient with a radioactive isotope that emits gamma rays as it
decays
Images are produced from the emissions collected by gamma ray
detectors
Positron Emission Tomography (PET)

18. Imaging in Radio Band
Magnetic Resonance Imaging (MRI)
• Place a patient in a powerful magnet and passes radio waves through his
or her body in short pulses.

19. Image Types

20. Some Common Image Formats and Their Characteristics
Format
•
jpg/jpeg (Joint Photographic Experts
Group)
•
Characteristics
•
Image compression, supports 8-bit per color
(RGB), generational degradation when edited
repeatedly.
tiff (Tagged-Image File Format)
•
Supports 8-bit and 16-bit per color , Support s
OCR and device-specific color schemes
•
Gif (Graphics Interchange Format)
•
Limited to 256 colors , Supports animation
•
png (Portable Network Graphics)
•
16 million colors (truecolor), Good for large
images, best suited for editing
•
bmp (Bit Map)
•
Simple, suited for all WINDOWS applications,
uncompressed

21. Few Basic Image Operations

22. Contrast Enhancement

23. Image Resolution

24. Scaling

25. Rotation

26. Translate

27. Reflect or Mirroring

28. Image Sharpening

29. Sharpening

30. Image Sharpening

31. Machine Vision System Components

32. Components of a Generic Machine Vision System
• Radiation source: Illuminating the
object/scene
• Camera: The optical lens
• Sensor: Converting the scene into a signal
• Processer: Playing with the signal
• Knowledge-Base: data understanding
• Action unit: responding the visual information

33. MV Schematic

34. Illumination + Camera + Sensor+ Signal

35. Processing Unit: Preprocessing

36. Processing Unit: Segmentation

37. Image Understanding: Tracking people’s activities

38. Image Understanding: Skin tracking

39. Image Understanding: Gesture Tracking

40. Comparison of Machine
&
Human Vision System

41. Human Vision versus Machine Vision Performance
Parameters
Functional Parameter
Human Vision
Machine Vision
Adaptability
More adaptable to
environmental conditions
Not much adaptable to
changing world
Decision Making
Humans are good in making
relative comparisons
Machine needs fixed
numerical values to decide
Consistency
Human are tired and less
consistent
Machines are consistent
Accuracy
Accuracy is subjective
Accuracy is higher
Speed
Human brain is fast in
processing
Machines with state of art
have limited speed
incomparable to human
brain
Spectrum
Human can make use of only
visible light (390-790mm)
Machines can operate in Xray and infra red ranges

42. That’s All for this Session