SECTION I: GENERAL INFORMATION ABOUT THE COURSE |
Course Code | Course Name | Year | Semester | Theoretical | Practical | Credit | ECTS |
60610MEEOS-CME0437 | Image Processing | 4 | Spring | 2 | 2 | 3 | 7 |
Course Type : | Elective Course IV |
Cycle: | Bachelor TQF-HE:6. Master`s Degree QF-EHEA:First Cycle EQF-LLL:6. Master`s Degree |
Language of Instruction: | English |
Prerequisities and Co-requisities: | N/A |
Mode of Delivery: | Face to face |
Name of Coordinator: | Profesör Dr. ABDURAZZAG ALI A ABURAS |
Dersin Öğretim Eleman(lar)ı: |
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Dersin Kategorisi: | Programme Specific |
SECTION II: INTRODUCTION TO THE COURSE |
Course Objectives: | 1. To familiarize students with digital image fundamentals and image transformations. 2. To Introduce algorithms for spatial and frequency domain filtering. 3. To teach students featured image processing applications with deep learning practices. |
Course Content: | D1) Digital image fundamentals (Image formation model, sampling, and quantization, representing digital images, spatial and intensity resolution, and image interpolations.) 2) Digital image fundamentals (Basic vector and matrix operations, single pixel and neighborhood operations, geometric transformations, image transforms) 3) Intensity Transformations (Fundamentals of intensity transformation, Image negatives, log and gamma transformations, contrast stretching, intensity level, and bit-plane Slicing.) 4) Intensity Transformations (Histogram processing, histogram equalization, histogram matching, local histogram processing, histogram statistics.) 5) Spatial filtering (Fundamentals of spatial filtering, spatial correlation and convolution, separable filter kernels). 6) Spatial filtering (Smoothing (low-pass) box and Gaussian kernels, sharpening (high-pass) filter kernels, image sharpening using first-second order derivatives.) 7) Filtering in the Frequency Domain (Complex numbers, Fourier series, Fourier transform of 1D continuous functions, sampling theorem, and Fourier transform of 1D sampled functions.) 8) Filtering in the Frequency Domain (2D continuous and discrete Fourier transform and its inverse, relationship between spatial and frequency intervals.) 9) Filtering in the Frequency Domain (2D discrete convolution theorem, frequency domain filtering fundamentals.) 10) Filtering in the Frequency Domain (Low-pass, high-pass, band-reject, and band-pass filters in the frequency domain.) 11) Image Restoration and Reconstruction (Noise models and estimating noise parameters, mean, order statistic, and adaptive filters.) |
Course Prerequisites: Programming skills (C/C++, VC++, Python, MATLAB) Good math background (Calculus, Linear Algebra, Statistical Methods) |
Knowledge (Described as Theoritical and/or Factual Knowledge.) | ||
Skills (Describe as Cognitive and/or Practical Skills.) | ||
1) The student will increase his/her knowledge and learning based each lecture related to the sequence of the given chapters during the semester weekly schedule. |
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2) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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3) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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4) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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5) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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6) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule |
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7) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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8) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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9) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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10) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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11) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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12) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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13) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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14) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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15) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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16) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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17) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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18) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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19) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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20) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule. |
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Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.) |
Course Notes / Textbooks: | Lecture material will be made available on OnlineBeykoz |
References: | 1) Recommended: Computer Vision: Algorithms and Applications Richard Szelisk, 2010 2) Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data by Sandipan Dey 2025, ISBN: 978-1789343731 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Programme Learning Outcomes | Contribution Level (from 1 to 5) | |
1) | Has sufficient knowledge in mathematics, science, computer science and computer engineering; use theoretical and applied knowledge in these fields together to solve computer engineering problems | |
2) | Uses and applies theoretical and applied sciences in the field of basic science subjects for the solution of computer engineering problems. | |
3) | Analyzes computer engineering applications, designs and develops models to meet specific requirements under realistic constraints and conditions. For this purpose, selects and uses appropriate methods, tools and technologies. | |
4) | Identify, define, formulate and solve complex computer engineering problems; for this purpose select and apply appropriate analytical and modeling methods | |
5) | Selects and effectively uses modern techniques and tools and information technologies required for computer science and computer engineering applications. | |
6) | Designs a complex computer and software based system, process, device or product to meet certain requirements under realistic constraints and conditions, including economics, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues; For this purpose, it applies modern design methods. | |
7) | Has information about the standards used in computer engineering applications. | |
8) | Owns the competencies required by the constantly developing field of computer engineering and the global competitive environment. | |
9) | Acquires communication in a Foreign Language (English) competence defined on the level of at least B1 in European Language Portfolio. (In programs whose medium of instruction is English, on the level of B2/B2+). |
SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE |
Lectures | |
Discussion | |
Views | |
Reading | |
Peer Education | |
Questions Answers | |
Active Participation in Class |
Measurement and Evaluation Methods | # of practice per semester | Level of Contribution |
Presentation | 1 | % 10.00 |
Project | 1 | % 15.00 |
Midterms | 1 | % 25.00 |
Semester Final Exam | 1 | % 50.00 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 50 | |
PERCENTAGE OF FINAL WORK | % 50 | |
Total | % 100 |
SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE |
WORKLOAD OF TEACHING & LEARNING ACTIVITIES | |||
Teaching & Learning Activities | # of Activities per semester | Duration (hour) | Total Workload |
Course | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 0 | 0 | 0 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 1 | 0 | 0 |
Homework Assignments | 0 | 0 | 0 |
Total Workload of Teaching & Learning Activities | - | - | 42 |
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES | |||
Assesment & Evaluation Activities | # of Activities per semester | Duration (hour) | Total Workload |
Quizzes | 2 | 0 | 0 |
Midterms | 1 | 2 | 2 |
Semester Final Exam | 1 | 2 | 2 |
Total Workload of Assesment & Evaluation Activities | - | - | 4 |
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) | 46 | ||
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) | 7 |