Course Unit Code | Course Unit Title | Type of Course | Year | Semester | ECTS |

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

- Görüntülerin ve diğer iki boyutlu işaretlerin fiziksel/matematiksel özellikleri ile ilgili ileri düzeyde bilgi edinmek
- Görüntülerin matematiksel dönüşünleri ile ilgili ileri düzeyde bilgi edinmek
- Maske kavramı ile FIR ve IIR görüntü işleme ile ilgili ileri düzeyde bilgi edinmek
- Görüntü işleme sistemlerin tasarım ve test süreçlerini Matlab destekli olarak gerçekleştirebilmek

Mode of Delivery

Formal Education

Prerequisites and co-requisities

None

Recommended Optional Programme Components

None

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)

None

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 Activities | Quantity | Weight (%) |

Midterm Examination | 1 | 100 |

SUM | 100 | |

End Of Term (or Year) Learning Activities | Quantity | Weight (%) |

Final Examination | 1 | 100 |

SUM | 100 | |

Term (or Year) Learning Activities | 40 | |

End Of Term (or Year) Learning Activities | 60 | |

SUM | 100 |

Workload Calculation

Activities | Quantity | Time(hours) | Total Workload(hours) |

Midterm Examination | 1 | 5 | 5 |

Final Examination | 1 | 5 | 5 |

Quiz | 4 | 3 | 12 |

Attending Lectures | 12 | 3 | 36 |

Self Study | 5 | 4 | 20 |

Individual Study for Homework Problems | 12 | 3 | 36 |

Individual Study for Mid term Examination | 3 | 5 | 15 |

Individual Study for Final Examination | 3 | 7 | 21 |

SUM | 150 |