Institute of Sciences
Electric Electronic Engineering (Thesis)
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Course CodeSemester Course Name LE/RC/LA Course Type Language of Instruction ECTS
EEY0308 3/0/0 DE 9
Course Goals
To know the basic components of an image processing system.
To understand how images are represented; including optical images, analog images, and digital images. Understand image types such as binary images, gray-scale images, color and multi-spectral images.
To understand why preprocessing is performed and know about image geometry, convolution masks, image algebra and basic spatial filters.
To understand image quantization in both the spatial and brightness domains.
To understand how discrete transforms work.
To understand lowpass, highpass, bandpass and notch filters.
To know the three categories of image processing applications: enhancement, restoration and compression.
Prerequisite(s) -
Corequisite(s) -
Special Requisite(s) -
Instructor(s) Assist. Prof. Dr. Ertuğrul Saatçı
Course Assistant(s)
Schedule Tuesday, 15:00-18:00, ELK Lab. 2
Office Hour(s) Asst.Prof. Dr. Ertuğrul Saatçı, Tuesday, 13:00-15:00, 2-D-17
Teaching Methods and Techniques The module will be delivered in a series of lectures, supported by practical sessions and self-directed study on the part of the student. The course is taught by lectures at the rate of 3 hours per week.

A part of the lectures will consist of delivery of the course material using powerpoint.

The lectures will follow a textbook and will contain supporting material for the practical sessions. The practical sessions will consist of  a set of  experiment using MATLAB programming language.

The lectures will include discussion questions which will be used to stimulate in-class discussion.
Principle Sources R. C. Gonzalez, R. E. Woods, Digital Image Processing, 4th edition, Pearson, 2017.

A. K. Jain, Fundamentals of Digital İmage Processing, Prentice Hall, Addison-Wesley, 1989.
Other Sources
Course Schedules
Week Contents Learning Methods
1. Week Introduction and Motivation oral presentation, case study
2. Week Visual perception, light and EM spectrum, Mathematical model of an image, Image sensing and acquisition oral presentation, case study
3. Week Linear Systems, Convolution, Correlation, Impulse Response oral presentation, case study
4. Week Fourier transform and its properties, The frequency concept in an image and its frequency spectrum, Sampling of an image, aliasing and conditions on sampling frequency, Construction of an image from sinusoidal plane waves oral presentation, case study
5. Week Fourier transform and its properties continued oral presentation, case study
6. Week Image Enhancement in the spatial domain: Pixel-Point Operations such as lightening, darkening, changing the contrast (histogram enhancement) oral presentation, case study
7. Week Image Enhancement in the spatial domain: Pixel-Group Operations such as convolution operation and related concepts as the convolution mask and the impulse response. oral presentation, case study
8. Week Midterm I oral presentation, case study
9. Week Image Enhancement in the frequency domain oral presentation, case study
10. Week Image Enhancement in the frequency domain continued oral presentation, case study
11. Week Edge detection (Prewitt, Roberts, Sobel, Laplacian, Canny, Hoteling) oral presentation, case study
12. Week Morphological operations oral presentation, case study
13. Week Midterm II oral presentation, case study
14. Week Color Image Processing oral presentation, case study
15. Week
16. Week
17. Week
Evaluation tools Quantity Weight(%)
Midterm(s) 2 50
Homework / Term Projects / Presentations 3 5
Attendance 14 5
Final Exam 1 40

Program Outcomes
Learning Outcomes
Course Assessment Matrix:
Program Outcomes - Learning Outcomes Matrix