| INFORMATION SYSTEMS ENGINEERING | |||||
|---|---|---|---|---|---|
| Qualification Awarded | Length of Program | Toplam Kredi (AKTS) | Mode of Study | Level of Qualification & Field of Study | |
| 4 | 240 | FULL TIME |
TQF, TQF-HE, EQF-LLL, ISCED (2011):Level 6 QF-EHEA:First Cycle TQF-HE, ISCED (1997-2013): 48,52 |
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| Course Code: | EEE457 | ||||||||
| Course Name: | DIGITAL IMAGE PROCESSING | ||||||||
| Course Semester: | Spring | ||||||||
| 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: | E-Learning | ||||||||
| Name of Coordinator: | Prof. Dr. Yıldıray YALMAN | ||||||||
| Course Lecturer(s): |
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| Course Assistants: |
| Course Objectives: | The objective of the course is to teach the digital image processing methods. By the end of the course, students will be able to: Identify the fundamentals of digital image processing Categorize image transform methods used in DIP Choose appropriate image enhancement techniques used in DIP Explain image restoration techniques and methods used in DIP Use image compression and segmentation used in DIP |
| Course Content: | This course covers the topics of introduction to digital image processing (DIP) principles, tools, techniques, and algorithms. Includes topics in image representation, analysis, filtering, and segmentation, and pattern recognition. It also includes teaching an image processing software (MATLAB) tools for some assignments. |
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The students who have succeeded in this course;
1) Explaining the main challenges behind the design of machine vision systems 2) Describing the general processes of image acquisition, storage, enhancement, segmentation, representation, and description. 3) Implement basic operations, filtering and enhancement algorithms for monochrome as well as color images using MATLAB |
| Week | Subject | Related Preparation |
| 1) | Digital image fundamentals | |
| 2) | Matlab-Image Processing toolbox and basic applications | |
| 3) | Basic concepts of the image processing: digital image, digital/analog video, pixel, resolution, bit depth, color concepts and formats. | |
| 4) | Image files; (raw, yuv, tiff, bmp, jpeg). Basic image operations: rotation, mirroring, translation, change in size (zoom). | |
| 5) | Image enhancement; brightness and contrast settings: Thresholding, negation, histogram, contrast stretching. | |
| 6) | Pixel Neighborhood operations; convolution, low-pass, high-pass filter, median (median) filter, edge detection, correlation. | |
| 8) | Color spaces: RGB, HSI, YUV, CMYK, etc. | |
| 8) | Midterm Exam | |
| 9) | Frequency domain, filtering, phase correlation | |
| 10) | Morphological operations: spreading, erosion, opening, closing | |
| 11) | Lossy and Lossless compression, JPEG | |
| 12) | Image segmentation | |
| 13) | Representation and Description | |
| 14) | Object recognition |
| 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. 3. A. Murat Tekalp, “Digital Video Processing”, Prentice Hall, 978-0131900752. 4. J.G. Proakis, D. G. Manolakis, “Digital Signal Processing: Principles, Algorithms, and Applications”, Prentice Hall, 978-0133737622. 5. S. Mitra, “Digital Signal Processing: A Computer-Based Approach”, McGraw-Hill, 978-0077366766 |
| Course Learning Outcomes | 1 |
2 |
3 |
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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) Ability to function on multi-disciplinary teams | ||||||||||||
| 5) An ability to identify, formulate, and solve engineering problems | ||||||||||||
| 6) An understanding of professional and ethical responsibility | ||||||||||||
| 7) An ability to communicate effectively | ||||||||||||
| 8) The broad education necessary to understand the impact of engineering solutions in a global and societal context | ||||||||||||
| 9) A recognition of the need for, and an ability to engage in life-long learning | ||||||||||||
| 10) A knowledge of contemporary issues | ||||||||||||
| 11) An ability to use the techniques, skills and modern engineering tools necessary for engineering practice | ||||||||||||
| 12) An ability to apply basic knowledge in database systems, networking, hardware, software, electronics, systems and contemporary topics in the context of Information Systems Engineering | ||||||||||||
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 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) | Ability to function on multi-disciplinary teams | |
| 5) | An ability to identify, formulate, and solve engineering problems | |
| 6) | An understanding of professional and ethical responsibility | |
| 7) | An ability to communicate effectively | |
| 8) | The broad education necessary to understand the impact of engineering solutions in a global and societal context | |
| 9) | A recognition of the need for, and an ability to engage in life-long learning | |
| 10) | A knowledge of contemporary issues | |
| 11) | An ability to use the techniques, skills and modern engineering tools necessary for engineering practice | |
| 12) | An ability to apply basic knowledge in database systems, networking, hardware, software, electronics, systems and contemporary topics in the context of Information Systems Engineering |
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 4 | % 15 |
| Homework Assignments | 3 | % 10 |
| Midterms | 1 | % 20 |
| Semester Final Exam | 1 | % 55 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 45 | |
| PERCENTAGE OF FINAL WORK | % 55 | |
| Total | % 100 | |
| Aktiviteler | Number of Activities | Duration (Hours) | Workload |
| Course | 14 | 3 | 42 |
| Homework Assignments | 3 | 5 | 15 |
| Quizzes | 4 | 3 | 12 |
| Midterms | 1 | 25 | 25 |
| Semester Final Exam | 1 | 30 | 30 |
| Total Workload | 124 | ||