BİLGİ PAKETİ / DERS KATALOĞU
| ELECTRICAL AND ELECTRONICS ENGINEERING (WITH - THESIS) | |||||
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| Qualification Awarded | Length of Program | Toplam Kredi (AKTS) | Mode of Study | Level of Qualification & Field of Study | |
| Master's ( Second Cycle) Degree | 2 | 120 | FULL TIME |
TQF, TQF-HE, EQF-LLL, ISCED (2011):Level 7 QF-EHEA:Second Cycle TQF-HE, ISCED (1997-2013): 52 |
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General Course Description Information
| Course Code: | EEE521 | ||||||||
| Course Name: | Advanced Digital Image Processing | ||||||||
| Course Semester: | Fall | ||||||||
| Course Credits: |
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| Language of instruction: | English | ||||||||
| Condition of Course: | |||||||||
| Does the Course Work Experience Require?: | No | ||||||||
| Course Type : | Bölüm/Program Seçmeli | ||||||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||||||
| Name of Coordinator: | Prof. Dr. Yıldıray YALMAN | ||||||||
| Course Lecturer(s): |
Prof. Dr. Yıldıray YALMAN |
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| Course Assistants: |
Objective and Contents of the Course
| Course Objectives: | The objective of the course is to teach the advanced digital image processing methods. By the end of the course, students will be able to: Realize the multidimensional signal processing Categorize image transform methods (DCT, DFT, Wavelet, etc.) used in DIP Choose appropriate image enhancement and restoration techniques used in DIP Explain steganography and watermarking methods used in DIP Use image compression and segmentation used in DIP |
| Course Content: | Course Description This course covers the topics of advanced digital image processing (DIP) principles, tools, techniques, and algorithms. Includes topics in image representation, analysis, filtering, and segmentation, pattern recognition, compression, steganography and digital watermarking. It also includes teaching an image processing software (MATLAB) tools for some assignments. |
Learning Outcomes
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The students who have succeeded in this course;
1) 1. Explain the main challenges behind the design of machine vision systems. 2) 2. Describe the general processes of image acquisition, storage, enhancement, segmentation, representation, and description. 3) 3. Implement DIP operations, filtering and enhancement algorithms for monochrome as well as color images using MATLAB. 4) 4. Implement digital steganography and watermarking algorithms. |
Ders Akış Planı
| Week | Subject | Related Preparation |
| 1) | Digital image fundamentals | |
| 2) | Matlab-Image Processing toolbox and basic applications | |
| 3) | Human Visual System (Modulation transfer function, visual masking, noise visibility, color vision, Distortion measures) | |
| 4) | Multidimensional Signal Processing (Vector and matrix image presentations, discrete and continuous Fourier transforms) | |
| 5) | Image Sensor Models (Optical, radar and medical coherent/noncoherent imaging applications: aperture diffraction constrains, defocusing, motion blur, atmospheric turbulence, sparse imaging apertures, Photographic film, Electronic imaging, CCD imaging applications, Smart sensors) | |
| 6) | Basic concepts of the image processing: digital image, digital/analog video, pixel, resolution, bit depth, color concepts and formats. | |
| 7) | Image Representation (wavelets), Random signals, Image Modeling (Edge and texture models, Doubly stochastic processes, Relationships between models) | |
| 8) | Midterm | |
| 9) | Pixel Neighborhood operations; convolution, low-pass, high-pass filter, median (median) filter, edge detection, correlation. | |
| 10) | Noise Models (Additive noise: Poisson, Gaussian and Laplacian models), Image Denoising (Maximum-likelihood estimation, Bayesian estimators, Models selection (MDL principle), Transform-based denoising: adaptive Wiener filtering) | |
| 11) | Image Restoration (Statistical ill-posed problems, Deterministic regularization: Tikhonov, edge-preserving and adaptive regularizations, Transform-based restoration, Blind deconvolution) | |
| 12) | Image Compression (Basics of source coding theory (lossless and lossy), Vector quantization, codebook design, Transform and subband coding) | |
| 13) | Digital Data Hiding (Steganography (secure communications), Digital watermarking | |
| 14) | Project presentatitons |
Sources
| Course Notes / Textbooks: | R.C. Gonzalez, R.E. Woods, S.L. Eddins, “Digital Image Processing Using Matlab”, Prentice Hall, 978-0130085191. |
| References: | 1. R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, 9780133356724, 2017. 2. Al Bovik, “The Essential Guide to Image Processing”, Elsevier, 2nd Edition, 978-0-12-374457-9. |
Contribution of The Course Unit To The Programme Learning Outcomes
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
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| Program Outcomes | ||||||||||||
| 1) An ability to apply knowledge of mathematics, science, and engineering | ||||||||||||
| 2) An ability to design and conduct experiments, as well as to analyze and interpret data | ||||||||||||
| 3) An ability to design a system, component or process to meet desired needs | ||||||||||||
| 4) An ability to identify, formulate, and solve engineering problems | ||||||||||||
| 5) An ability to use the techniques, skills and modern engineering tools necessary for engineering practice | ||||||||||||
| 6) Çok disiplinli takım çalışması yürütebilme becerisi | ||||||||||||
| 7) A recognition of the need for, and an ability to engage in life-long learning | ||||||||||||
| 8) A knowledge of contemporary issues | ||||||||||||
| 9) An ability to apply engineering knowledge in electric and electronics | ||||||||||||
| 10) An understanding of professional and ethical responsibility | ||||||||||||
| 11) An ability to communicate effectively | ||||||||||||
| 12) The broad education necessary to understand the impact of engineering solutions in a global and societal context | ||||||||||||
Course - Learning Outcomes
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | An ability to apply knowledge of mathematics, science, and engineering | 3 |
| 2) | An ability to design and conduct experiments, as well as to analyze and interpret data | 2 |
| 3) | An ability to design a system, component or process to meet desired needs | |
| 4) | An ability to identify, formulate, and solve engineering problems | |
| 5) | An ability to use the techniques, skills and modern engineering tools necessary for engineering practice | |
| 6) | Çok disiplinli takım çalışması yürütebilme becerisi | |
| 7) | A recognition of the need for, and an ability to engage in life-long learning | |
| 8) | A knowledge of contemporary issues | |
| 9) | An ability to apply engineering knowledge in electric and electronics | |
| 10) | An understanding of professional and ethical responsibility | |
| 11) | An ability to communicate effectively | |
| 12) | The broad education necessary to understand the impact of engineering solutions in a global and societal context |
Learning Activities and Teaching Methods
Assessment & Evaluation Methods of the Course Unit
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 5 | % 10 |
| Homework Assignments | 3 | % 10 |
| Project | 1 | % 15 |
| Seminar | 1 | % 10 |
| Midterms | 1 | % 20 |
| Semester Final Exam | 1 | % 35 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 65 | |
| PERCENTAGE OF FINAL WORK | % 35 | |
| Total | % 100 | |
Workload & ECTS Credits of The Course Unit
| Aktiviteler | Number of Activities | Duration (Hours) | Workload |
| Course | 14 | 3 | 42 |
| Presentations / Seminar | 1 | 25 | 25 |
| Project | 1 | 25 | 25 |
| Homework Assignments | 3 | 8 | 24 |
| Quizzes | 5 | 4 | 20 |
| Midterms | 1 | 25 | 25 |
| Semester Final Exam | 1 | 35 | 35 |
| Total Workload | 196 | ||