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
  To be a junior (3th year) student
Course Language
English
Course Type
Elective
Course Level
First Cycle
Mode of Delivery -
Teaching Methods and Techniques of the Course
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.
Learning Outcomes 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 Description 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.
Related Sustainable Development Goals

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
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
-
-
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterm
2
60
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
2
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
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
16
3
48
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exam
Midterms
2
15
Final Exams
1
24
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
Program Competencies/Outcomes
* Contribution Level
1
2
3
4
5
1

To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems.

X
2

To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. 

X
4

To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently.

X
5

To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas.

X
6

To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually.

X
7

To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions

8

To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions.

X
9

To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice.

10

To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development.

11

To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language.

12

To be able to speak a second foreign at a medium level of fluency efficiently.

13

To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering.

X

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