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| BILB3003 | Digital Image Processing | 4+0+0 | ECTS:5 | | Year / Semester | Fall Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | COMPUTER SCIENCE | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Prof. Dr. Orhan KESEMEN | | Co-Lecturer | Department Faculty Members
| | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To learn image processing techniques using mathematics, statistics and computer science. To gain the ability to develop package programs. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Easier to understand image processing programs. | 9,11,12 | 1,3, | | LO - 2 : | To learn image processing techniques. | 9,11,12 | 1,3, | | LO - 3 : | To learn the use of theoretical sciences such as mathematics and statistics in their application areas. | 9,11,12 | 1,3, | | LO - 4 : | To learn how to write package programs. | 9,11,12 | 1,3, | | 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 | | |
| Introduction to image processing, use of color components, color spaces, digitization and discontinuous operations; Point Operations: arithmetic and binary operations, histogram equalization, density transformations, discrimination improvement; Areal Operations: concepts of involution and relation, median, common, and other statistical filters; Positional transformations, internal estimation, scaling, rotation, translation; Layered operations, arithmetic quadratic, proportional; Additive transformations, Cosine, Fourier, Walsh, Hadamard transforms. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to image processing, digitization and discontinuous processes; | | | Week 2 | Introduction to Python | | | Week 3 | Introduction to computer vision with Python | | | Week 4 | Use of color components, color spaces, | | | Week 5 | Point Operations: arithmetic and binary operations, histogram equalization and fitting, | | | Week 6 | Point Operations II: density transformations, discrimination improvement; | | | Week 7 | Areal Operations I: concepts of convolution and relation, | | | Week 8 | Areal Operations II: median, common, and other statistical filters; | | | Week 9 | Midterm Exam | | | Week 10 | Positional transformations: internal estimation, scaling, rotation, displacement, cropping, flipping; | | | Week 11 | Formal transformations: variable scaling, rotation and displacement, | | | Week 12 | Layered operations, arithmetic, quadratic, complex, binary and proportional operations; | | | Week 13 | Additive transformations I: Cosine transformation. | | | Week 14 | Additive transforms II: Fourier transform | | | Week 15 | Additive transformations III: Walsh and Hadamard transformations. | | | Week 16 | Final exam | | | |
| 1 | Rafael C. Gonzalez and Richard E. Woods, 1992; Digital Image Processing, Addision-Wesley, New York | | | |
| 1 | Tinku Acharya and Ajoy K. Ray, 2005; Image Processing: Principles and Applications, Wiley, | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | 30 | | Homework/Assignment/Term-paper | 2,3,4,5,6,7,8,10,11,12 | | 10 | 20 | | 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 | 4 | 14 | 56 | | Sınıf dışı çalışma | 1 | 14 | 14 | | Arasınav için hazırlık | 1 | 8 | 8 | | Arasınav | 1 | 1 | 1 | | Ödev | 1 | 14 | 14 | | Dönem sonu sınavı için hazırlık | 1 | 6 | 6 | | Dönem sonu sınavı | 1 | 1 | 1 | | Total work load | | | 100 |
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