| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Image Processing | EOS609 | Elective | Doctorate degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Özcan GÜNDOĞDU
Associate Prof. Dr. Erhan AKMAN
Associate Prof. Dr. Belgin GENÇ ÖZTOPRAK
Learning Outcomes of the Course Unit
1) Recognize image production mechanisms and standards.
2) Practise improving image quality applications.
3) Defines the methods of image data compression.
4) Explain linear filtering, linear transformations, mathematical morphology.
5) Explain the topics of edge detecting and geometric diffusion.
Program Competencies-Learning Outcomes Relation
| Program Competencies | |||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | ||
| Learning Outcomes | |||||||||||||
| 1 | High | High | High | Middle | Middle | Middle | High | High | High | High | High | High | |
| 2 | High | Middle | High | Low | High | High | Middle | Middle | Middle | Middle | High | High | |
| 3 | High | High | Middle | High | High | High | Low | Middle | High | Middle | Middle | High | |
| 4 | High | Middle | High | High | Middle | High | High | High | High | High | High | Middle | |
| 5 | High | High | High | High | Middle | Middle | Middle | Middle | High | High | High | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Photonics, Optical Materials, Optical Design, Electro-Optical Materials and Systems, Geometric Optics, Waveguide Optics, Sensors and Applications, Advanced Robotics and Automation Systems, Electromagnetic Wave Propagation and Scattering, Satellite Communication Systems, Nano-Biophotonic, Engineering Mathematics, Advanced Laser Applications, Electro-Optical Systems Laboratory
Course Contents
Image production mechanisms and standards; 2-Dimensional and 3-Dimensional image production, digital image formats, geometric relations between image and Earth platform. Image Analysis: Digital zooming, image algebra, spatial filters, edge detection operators; Image Partitioning; Discrete Transforms (Fourier, Cosine, Walsh-Hadamard, Wavelet transform); Model-based object detection with Hough transform; Generation and analysis of property parameters of objects in binary images. Mathematical Morphology; Image restoration, Spatial and spectral filtering techniques; Geometric transformations. Improving image quality; Image data tightening; lossy-lossless image data compression methods, linear filtering; linear transformations; mathematical morphology; compression; inverse problems in imaging; image enhancement; finding an edge; feature extraction and geometric diffusion.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
1) Lecture
2) Question-Answer
3) Discussion
4) Drill and Practice
5) Group Study
6) Self Study
Assessment Methods and Criteria
Language of Instruction
Turkish
Work Placement(s)
Not Required