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| TBIL2029 | Image processing | 2+1+0 | ECTS:3 | | Year / Semester | Fall Semester | | Level of Course | Short Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER TECHNOLOGIES | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 2 hours of lectures and 1 hour of practicals per week | | Lecturer | Dr. Öğr. Üyesi Ercüment YILMAZ | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to teach students the analytical tools and methods currently used in digital image processing, as applied to image information for human imaging, and to enable them to perform applications in the laboratory for image restoration, enhancement, and compression using these tools. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | They can gain skills in fundamental algorithmic functions commonly used in image processing. | 6 | 1, | | LO - 2 : | They will be able to understand the functions and organization of system design and algorithm development in image-based applications. | 6 | 1, | | LO - 3 : | They can gain the application skills to transfer the learned functions and approaches to real-life and interdisciplinary studies. | 6 | 1, | | LO - 4 : | They can acquire skills in lossy or lossless data reduction of image and video data. | 6 | 1, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| The aim of this course is to teach students the analytical tools and methods currently used in digital image processing, as applied to image information for human imaging, and to enable them to perform applications in the laboratory for image restoration, enhancement, and compression using these tools. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Overview of Computer Imaging Systems | | | Week 2 | Image analysis, preprocessing, matrices, and vectors. | | | Week 3 | Human visual system, image model, Python, Matlab, OpenCV | | | Week 4 | Image enhancement, grayscale modes, histogram mode
| | | Week 5 | Discrete transforms, Fourier | | | Week 6 | Image file types; Bitmap and JPEG. | | | Week 7 | Filtering, wavelet transform, pseudocolor | | | Week 8 | Image enhancement, sharpening, smoothing | | | Week 9 | Mid-term exam | | | Week 10 | Image restoration, overview, system model, noise reduction: ordering filters. | | | Week 11 | Image restoration: noise reduction: averaging and adaptive filters, decay modeling, inverse filter. | | | Week 12 | Frequency. Filters, geometric transformations. | | | Week 13 | Image compression: system model, lossless methods | | | Week 14 | Image compression: lossy methods, working on the project
| | | Week 15 | In-class activities | | | Week 16 | Final exam
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| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 50 | | End-of-term exam | 16 | | 1 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 1 | 14 | 14 | | Arasınav için hazırlık | 1 | 7 | 7 | | Arasınav | 1 | 1 | 1 | | Dönem sonu sınavı için hazırlık | 1.5 | 6 | 9 | | Dönem sonu sınavı | 1 | 1 | 1 | | Diğer 1 | 1 | 1 | 1 | | Total work load | | | 75 |
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