| Course Code |
CSC399 |
| Course Title |
Digital Image Processing |
| Credit Hours |
2+1 |
| Prerequisites by Course(s) and Topics |
None |
| Assessment Instruments with Weights (homework, quizzes, midterms, final, programming assignments, lab work, etc.) |
SESSIONAL (Quizzes, Assignments, Presentations) =25 %
Midterm Exam =25 %
Final Exam = 50%
|
| Course Coordinator |
Engr. Nadeem Ali |
| URL (if any) |
NA |
| Current Catalog Description |
|
| Textbook (or Laboratory Manual for Laboratory Courses) |
Digital Image Processing Third Edition by Rafael C. Gonzalez , Richard E. Woods |
| Reference Material |
• Digital Image Processing Using Matlab (Third Edition) by Rafael C. Gonzalez , Richard E. Woods |
| Course Goals |
The main aim of this exam is to introduce the field of image processing. The students learn about the mathematical as well as the practical side of image processing. They acquire knowledge about fundamental principles of image processing and about practical approaches of image processing. |
| Course Learning Outcomes (CLOs): |
| At the end of the course the students will be able to: | Domain | BT Level* |
|---|
| Understand the basics, applications in general, working inside the digital camera, sampling and quantization, image representation, etc. |
C |
1.2 |
| Implement image enhancement, image segmentation, image transformations, spatial and frequency domain processing, filtering, convolution, image registration, feature detection, pattern recognition, etc. |
C |
3 |
| Evaluate the performance of different image processing algorithms. |
C |
4.5 |
| * BT= Bloom’s Taxonomy, C=Cognitive domain, P=Psychomotor domain, A= Affective domain |
|
|
|
| Topics Covered in the Course, with Number of Lectures on Each Topic (assume 15-week instruction and one-hour lectures) |
| Week | Lecture | Topics Covered |
|---|
| Week 1 |
1 |
What is Digital Image Processing? |
|
2 |
Fundamental Steps in Image Processing |
| Week 2 |
3 |
Component of Image processing |
|
4 |
Element of Visual Perception, Lights and Electromagnetic, Image Sensing and Acquisition Spectrum |
| Week 3 |
5 |
Image Sampling and Quantization, Some Basic Relationships between Pixels |
|
6 |
An Introduction to the Mathematical Tools Used in Digital Image Processing |
| Week 4 |
7 |
Intensity Transformations, Spatial Filtering & Background |
|
8 |
Some Basic Intensity Transformation Functions |
| Week 5 |
9 |
Negative Image Transformation, Power law, |
|
10 |
Histogram Processing, Histogram Matching |
| Week 6 |
11 |
Fundamentals of Spatial Filtering |
|
12 |
Introduction to Filters |
| Week 7 |
13 |
Smoothing Spatial Filters |
|
14 |
Sharpening Spatial Filters |
| Week 8 |
1 hours |
Mid Term |
| Week 9 |
15 |
Mid Term Exam |
|
16 |
Mid Term Exam |
| Week 10 |
17 |
Filtering in the Frequency Domain, Background Of Frequency Domain |
|
18 |
Preliminary Concepts of Frequency Domain |
| Week 11 |
19 |
Sampling and the Fourier Transform |
|
20 |
Types of Fourier Transform |
| Week 12 |
21 |
Extension to Functions of Two Variables |
|
22 |
Some Properties of the 2-D Discrete Fourier Transform, Example of 2-D Discrete Fourier Transform |
| Week 13 |
23 |
The Basics of Filtering in the Frequency Domain |
|
24 |
Image Smoothing Using Frequency Domain Filters, |
| Week 14 |
25 |
Image Sharpening Using Frequency Domain Filters |
|
26 |
Noise Models |
| Week 15 |
27 |
Image Restoration and Reconstruction |
|
28 |
A Model of the Image Degradation/Restoration Process |
| Week 16 |
29 |
Color Image Models |
|
30 |
Edge detection techniques |
| Week 17 |
2 hours |
Final Term |
|
| Laboratory Projects/Experiments Done in the Course |
Term Project |
| Programming Assignments Done in the Course |
MATLAB based programming assignment |
