ACADEMICS
Course Details
ELE490 - Fundamentals of Image Processing
2023-2024 Spring term information
The course is not open this term
ELE490 - Fundamentals of Image Processing
| Program | Theoretýcal hours | Practical hours | Local credit | ECTS credit |
| Undergraduate | 3 | 2 | 4 | 7 |
| Obligation | : | Elective |
| Prerequisite courses | : | ELE301 |
| Concurrent courses | : | - |
| Delivery modes | : | Face-to-Face |
| Learning and teaching strategies | : | Lecture, Question and Answer, Problem Solving, Programming |
| Course objective | : | This course provides an introduction to the theory and applications of image processing using Python as the programming language. Topics include image formation, image transforms, image filtering, image enhancement, image restoration, segmentation, compression and color image processing. Concepts of pattern recognition such as object recognition, deep learning and clustering will be studied as applied to image processing. |
| Learning outcomes | : | A student who completes the course successfully is knowledgeable about image acquisition and formation, is able to filter an image in both the spatial and the frequency domains; restore, segment, and compress images using a programming language; and perform image classification and object recognition using deep learning. The student will have both theoretical and practical knowledge of image processing and apply his/her knowledge to real-world problems. |
| Course content | : | Python as a programming language, Image Acquisition and Digital Image Formation, Intensity Transformations, Spatial Filtering, Filtering in the Frequency Domain, Image Restoration and Reconstruction, Color Image Processing, Image Compression, Morphological Image Processing, Image Segmentation, Deep Learning, Image Classification and Object Recognition using Deep Learning. |
| References | : | Rafael C. Gonzalez & Richard E. Woods, Digital Image Processing, Addison Wesley. Practical Machine Learning and Image Processing, Himanshu Singh, Apres, 2019. |
| Weeks | Topics |
|---|---|
| 1 | Introduction to Image Processing, Introduction to Python Programming: Fundamentals |
| 2 | Introduction to Python Programming: Data Science Tools |
| 3 | Image Acquisition and Digital Image Formation |
| 4 | Intensity Transformations |
| 5 | Spatial Filtering |
| 6 | Filtering in the Frequency Domain |
| 7 | Image Restoration |
| 8 | Midterm |
| 9 | Color Image Processing |
| 10 | Image Compression |
| 11 | Morphological Image Processing |
| 12 | Image Segmentation |
| 13 | Fundamentals of Deep Learning |
| 14 | Image Classification and Object Detection using Deep Learning |
| 15 | Final exam preparation |
| 16 | Final exam |
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 0 | 0 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 4 | 20 |
| Presentation | 0 | 0 |
| Project | 1 | 10 |
| Seminar | 0 | 0 |
| Quiz | 0 | 0 |
| Midterms | 1 | 30 |
| Final exam | 1 | 40 |
| Total | 100 | |
| Percentage of semester activities contributing grade success | 60 | |
| Percentage of final exam contributing grade success | 40 | |
| Total | 100 | |
| Course activities | Number | Duration (hours) | Total workload |
|---|---|---|---|
| Course Duration | 14 | 3 | 42 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, etc.) | 14 | 5 | 70 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 1 | 30 | 30 |
| Homework assignment | 4 | 3 | 12 |
| Quiz | 0 | 0 | 0 |
| Midterms (Study Duration) | 1 | 25 | 25 |
| Final Exam (Study duration) | 1 | 40 | 40 |
| Total workload | 35 | 106 | 219 |
| Key learning outcomes | Contribution level | |||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1. | Possesses the theoretical and practical knowledge required in Electrical and Electronics Engineering discipline. | |||||
| 2. | Utilizes his/her theoretical and practical knowledge in the fields of mathematics, science and electrical and electronics engineering towards finding engineering solutions. | |||||
| 3. | Determines and defines a problem in electrical and electronics engineering, then models and solves it by applying the appropriate analytical or numerical methods. | |||||
| 4. | Designs a system under realistic constraints using modern methods and tools. | |||||
| 5. | Designs and performs an experiment, analyzes and interprets the results. | |||||
| 6. | Possesses the necessary qualifications to carry out interdisciplinary work either individually or as a team member. | |||||
| 7. | Accesses information, performs literature search, uses databases and other knowledge sources, follows developments in science and technology. | |||||
| 8. | Performs project planning and time management, plans his/her career development. | |||||
| 9. | Possesses an advanced level of expertise in computer hardware and software, is proficient in using information and communication technologies. | |||||
| 10. | Is competent in oral or written communication; has advanced command of English. | |||||
| 11. | Has an awareness of his/her professional, ethical and social responsibilities. | |||||
| 12. | Has an awareness of the universal impacts and social consequences of engineering solutions and applications; is well-informed about modern-day problems. | |||||
| 13. | Is innovative and inquisitive; has a high level of professional self-esteem. | |||||
1: Lowest, 2: Low, 3: Average, 4: High, 5: Highest