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| ELK7361 | Advanced Image Processing | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of ELECTRICAL and ELECTRONICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Mehmet ÖZTÜRK | | Co-Lecturer | Prof. Dr. Ali GANGAL | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The objective of this course is to give the graduate students a fundamental knowledge of the major topics of digital image processing: representation, processing techniques, and communications. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | have general knowledge on digital image processing | 1,2,3,6 | 1, | | PO - 2 : | do image enhancement and image restoration applications | 1,2,3,6 | 1 | | PO - 3 : | have the information about image compression and coding standards | 1,2,3,6 | 1 | | PO - 4 : | do color image processing applications | 1,2,3,6 | 1 | | PO - 5 : | understand recent developments on image processing applications | 1,2,3,7 | 1 | | PO - 6 : | performs examples from various image processing applications in Matlab environment | 1,2,3,7 | 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), PO : Learning Outcome | | |
| Introduction. Digital Image Fundamentals. Image Enhancement in the Spatial Domain. Image Enhancement in the Frequency Domain. Image Restoration. Color Image Processing. Wavelets and Multiresolution Processing. Image Compression. Morphological Image Processing. Image Segmentation. Representation and Description. Object Recognition. 3D vision Models. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction. Digital Image Fundamentals. | | | Week 2 | Image Enhancement in the Spatial Domain. | | | Week 3 | Image Enhancement in the Spatial Domain. | | | Week 4 | Image Enhancement in the Frequency Domain. | | | Week 5 | Image Restoration. | | | Week 6 | Color Image Processing. | | | Week 7 | Wavelets and Multiresolution Processing. | | | Week 8 | Image Compression. | | | Week 9 | Mid-term exam | | | Week 10 | Morphological Image Processing. | | | Week 11 | Image Segmentation. | | | Week 12 | Representation and Description. | | | Week 13 | Object Recognition. | | | Week 14 | 3D vision Models. | | | Week 15 | 3D vision Models. | | | Week 16 | End-of-term exam | | | |
| 1 | Gonzalez, R. C., Woods, R. E., 2008, "Digital Image Processing", Pearson Prentice Hall | | | |
| 1 | Gonzalez, R. C., Woods, R. E., Eddins, S. L., 2004, ?Digital Image Processing using MATLAB?, Prentice Hall. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Homework/Assignment/Term-paper | 5,6,7,8,10,11,12,13 | | 1 | 20 | | End-of-term exam | 16 | | 2 | 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 | 8 | 14 | 112 | | Arasınav için hazırlık | 10 | 1 | 10 | | Arasınav | 2 | 1 | 2 | | Ödev | 20 | 1 | 20 | | Dönem sonu sınavı için hazırlık | 12 | 1 | 12 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 200 |
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