Ondokuz Mayıs Üniversitesi Bilgi Paketi - Ders Kataloğu

Description of Individual Course Units

Course Unit CodeCourse Unit TitleType of CourseYearSemesterECTS
RAD606 Advanced Image Processing Techniques-II 927001 1 1 6
Level of Course Unit
Second Cycle
Objectives of the Course
To teach basics of image processing, observing those basics on Matlab development environment and gives students the ability to design image processing systems.
Name of Lecturer(s)
Yrd.Doç.Dr. Nurettin ŞENYER
Learning Outcomes
  1. Görüntülerin ve diğer iki boyutlu işaretlerin fiziksel/matematiksel özellikleri ile ilgili ileri düzeyde bilgi edinmek
  2. Görüntülerin matematiksel dönüşünleri ile ilgili ileri düzeyde bilgi edinmek
  3. Maske kavramı ile FIR ve IIR görüntü işleme ile ilgili ileri düzeyde bilgi edinmek
  4. Görüntü işleme sistemlerin tasarım ve test süreçlerini Matlab destekli olarak gerçekleştirebilmek
Mode of Delivery
Formal Education
Prerequisites and co-requisities
Recommended Optional Programme Components
Recommended or Required Reading
Two-Dimensional Signal and Image Processing, J S Lim, Prentice Hall, 1990 Digital Image Processing, R C Gonzales and R E Woods, Addison Wesley, 1992 Mashine Vision and Digital Image Processing, Prentice Hall, 1990 Cellular Neural Networks & Visual Computing, L. O. Chua, T Roska, World Scientific Pub Co; 1998
Planned Learning Activities and Teaching Methods
Language of Instruction
Work Placement(s)
Course Contents
Mathematical model of an image, the frequency concept in an image and its 2-D frequency spectrum, sampling of an image, aliasing and conditions on sampling frequency, separability in 2-D signals, periodicity concept in an image, expansion of an image into Fourier series, construction of an image from its harmonics, the 2-D Fourier transform, the Fourier transform of separable images, the z-transform and transfer function, the linear operations applied to an image: convolution, mask and impulse response, 2-D FIR filters: low-pass, high-pass, band-pass filters, methods of image enhancement, 2-D IIR filters: recursive computability and its conditions, other operations applied to images, cellular neural networks and their applications in 2-D filtering, other applications of cellular neural networks in image processing
Weekly Detailed Course Contents
Week Theoretical Practice Laboratory
1.The concepts of analog and digital images, creating images in Matlab, separability of 2-D signals
2.Frequency concept of images
3.Sampling of 2-D signals
4.Conditions of the sampling frequency
5.Periodicity, orientation and direction concepts of an image, Fourier series of an image, Matlab examples
6.Reconstructing an image using its Fourier series components, Matlab examples
7.2-D Fourier transform, Fourier transform of separable images, Matlab examples
8.Applying 2-D Fourier transform to images, Matlab examples
9.2-D FIR filters: low-, high- and band-pass filters, Matlab examples
10.Edge enhancements of images, Matlab examples
11.Median filters, Matlab examples
12.2-D IIR filters, recursive computability conditions, Matlab examples
13.Obtaining histogram data of an image, histogram operations, Matlab examples
14.Introduction of cellular neural networks
15.Applications of cellular neural networks in 2-D filtering, Matlab examples
16.Final exam
Assessment Methods and Criteria
Term (or Year) Learning ActivitiesQuantityWeight (%)
Midterm Examination1100
End Of Term (or Year) Learning ActivitiesQuantityWeight (%)
Final Examination1100
Term (or Year) Learning Activities40
End Of Term (or Year) Learning Activities60
Workload Calculation
ActivitiesQuantityTime(hours)Total Workload(hours)
Midterm Examination155
Final Examination155
Attending Lectures12336
Self Study5420
Individual Study for Homework Problems12336
Individual Study for Mid term Examination3515
Individual Study for Final Examination3721