CE 490 | Course Introduction and Application Information

Course Name
Introduction to Digital Image Processing
Code
Semester
Theory
(hour/week)
Application/Lab
(hour/week)
Local Credits
ECTS
CE 490
Fall/Spring
3
0
3
5

Prerequisites
None
Course Language
English
Course Type
Elective
Course Level
First Cycle
Course Coordinator
Course Lecturer(s)
Assistant(s) -
Course Objectives This course introduces the fundamental principles and algorithms of digital image processing systems. The course will cover many subjects including image sampling and quantization; spatial and frequency domain image enhancement techniques; digital signal processing theories used for digital image processing, such as onedimensional and twodimensional convolution, and twodimensional Fourier transformation; color models and basic color image processing.
Course Description The students who succeeded in this course;
  • will be able to process images using techniques of smoothing, sharpening, histogram processing, and filtering,
  • will be able to explain sampling and quantization processes in obtaining digital images from continuously sensed data,
  • will be able to enhance digital images using filtering techniques in the spatial domain,
  • will be able to enhance digital images using filtering techniques in the frequency domain,
  • will be able to restore images in the presence of only noise through filtering techniques,
  • will be able to describe most commonly applied color models and their use in basic color image processing,
  • will be able to write Matlab codes using image processing toolbox.
Course Content The following topics will be included: Digital images as twodimensional signals; twodimensional convolution, Fourier transform, and discrete cosine transform; Image processing basics; Image enhancement; Image restoration; Wavelets and Multiresolution processing; Image coding and compression.

 



Course Category

Core Courses
Major Area Courses
Supportive Courses
X
Media and Management Skills Courses
Transferable Skill Courses

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
1 Introduction. What is Digital Image Processing? Application areas of digital image processing Chapter 1. Sections 1.1-1.3. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
2 Digital Image Fundamentals. How digital images are generated? Sampling, quantization, aliasing, Moire patterns, image zooming and shrinking Chapter 1-2. Sections 1.4,1.5, 2.1-2.4. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
3 Digital Image Fundamentals. How digital images are generated? Sampling, quantization, aliasing, Moire patterns, image zooming and shrinking Chapter 1-2. Sections 1.4,1.5, 2.1-2.4. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
4 Human visual system Chapter 2. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
5 Image Enhancement in the spatial domain. Basic gray level transformations. Smoothing and sharpening spatial filters. Chapter 3. Sections 3.1-3.6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
6 Image Enhancement in the spatial domain. Histogram processing. Chapter 3. Sections 3.1-3.6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
7 The 2D Discrete Fourier Transform and Its Inverse, Properties of the 2D DFT and the 2D Convolution Theorem Chapter 4. Sections 4.5.5, 4.6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
8 The 2D Discrete Fourier Transform and Its Inverse, Properties of the 2D DFT and the 2D Convolution Theorem Chapter 4. Sections 4.5.5, 4.6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
9 Mid-term Exam Chapter 4. Sections 4.74.10. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
10 Image Enhancement in the frequency domain. Chapter 4. Sections 4.7-4.10. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
11 Image Enhancement in the frequency domain. Chapter 4. Sections 4.7-4.10. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
12 Image restoration: system model, noise model, estimation of degradation function. Chapter 5. Sections 5.1,5.2,5.7-5.10. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
13 Image restoration in the presence of noise only, inverse filtering, minimum mean square error (Wiener) filtering. Chapter 5. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
14 Color Image Processing. Color transformations. Color image smoothing and sharpening Chapter 6. Section 6.1-6.6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
15 Color Image Processing. Color transformations. Color image smoothing and sharpening Chapter 6. Section 6.1-6.6. Digital Image Processing. Gonzalez & Woods. ISBN 013168728X
16 Review of the semester

 

Course Notes/Textbooks R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, PrenticeHall, 3rd Ed., 2008, ISBN 013168728X.
Suggested Readings/Materials R. C. Gonzalez, R. E. Woods, S. L. Eddins, “Digital Image Processing Using MATLAB”, PrenticeHall, 2nd Ed., 2009, ISBN 9780982085400.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
-
-
Laboratory / Application
1
30
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
1
10
Project
1
30
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral Exam
Total

Weighting of Semester Activities on the Final Grade
3
90
Weighting of End-of-Semester Activities on the Final Grade
1
10
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Course Hours
Including exam week: 16 x total hours
16
3
48
Laboratory / Application Hours
Including exam week: 16 x total hours
16
Study Hours Out of Class
15
3
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
1
10
Project
1
37
Seminar / Workshop
Portfolios
Midterms / Oral Exams
1
30
Final / Oral Exam
    Total
170

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1 Adequate knowledge in Mathematics, Science and Software Engineering; ability to use theoretical and applied information in these areas to model and solve Software Engineering problems X
2 Ability to identify, define, formulate, and solve complex Software Engineering problems; ability to select and apply proper analysis and modeling methods for this purpose X
3 Ability to design, implement, verify, validate, measure and maintain a complex software system, process or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern methods for this purpose X
4 Ability to devise, select, and use modern techniques and tools needed for Software Engineering practice
5 Ability to design and conduct experiments, gather data, analyze and interpret results for investigating Software Engineering problems
6 Ability to work efficiently in Software Engineering disciplinary and multi-disciplinary teams; ability to work individually
7 Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of two foreign languages
8 Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself
9 Awareness of professional and ethical responsibility
10 Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development
11 Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of Software Engineering solutions

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest