COMPUTER ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code: | 1410002025 | ||||||||
Ders İsmi: | Image processing | ||||||||
Ders Yarıyılı: | Spring | ||||||||
Ders Kredileri: |
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Language of instruction: | TR | ||||||||
Ders Koşulu: | |||||||||
Ders İş Deneyimini Gerektiriyor mu?: | No | ||||||||
Type of course: | Bölüm Seçmeli | ||||||||
Course Level: |
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Mode of Delivery: | Face to face | ||||||||
Course Coordinator : | Dr.Öğr.Üyesi Recep DURANAY | ||||||||
Course Lecturer(s): | |||||||||
Course Assistants: |
Course Objectives: | Within the scope of this course, it is aimed to teach advanced digital signal, image processing, pattern recognition and machine learning methods on biomedical data. The main aim of the course is to increase students' mathematical, scientific and computational analysis skills in this field. In this context, the content of the course includes the acquisition of biomedical data, evaluation of its properties, teaching the reasons and applications of the preprocessing steps (noise removal, filtering, reinforcement, size reduction, etc.), feature extraction, modeling, unsupervised and supervised learning, as well as semi-trained, community and Deep learning issues will also be discussed. In addition, Matlab and Python-based individual/group projects will be carried out on basic biomedical applications in order to increase the computational skills of the students. |
Course Content: | Properties of biomedical signs and images; Transformation methods used in signal and image processing; Noise removal in signs and images; Signal and image filtering methods; Signal and image filtering methods; Linear and nonlinear dimension reduction methods; Statistical, morphological and spatial feature extraction methods; Instructed learning methods in signal and image processing; Unsupervised learning methods in signal and image processing; Semi-tutorial, community and deep learning methods. |
The students who have succeeded in this course;
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Week | Subject | Related Preparation |
1) | Obtaining and characteristics of biomedical signs and images | |
2) | Analysis of statistical characteristics of signals (Moments, power, information, correlation...) | |
3) | Digital signal processing fundamentals, sampling, quantization | |
4) | Frequency analysis, Conversion methods I: DFT, DCT, STFT | |
5) | Transformation methods II: Wavelet transform | |
6) | Image processing basics | |
7) | Noise removal in image processing | |
8) | Midterm | |
9) | Filtering and consolidation methods | |
10) | Analysis of signs and images with supervised learning methods | |
11) | Dimension reduction and linear/non-linear transformation methods | |
12) | Fundamentals of pattern recognition and machine learning for biomedical signs and images | |
13) | Analysis of signs and images with unsupervised learning methods | |
14) | Analysis of signs and images with supervised learning methods | |
15) | Analysis of signs and images with semi-tutorial, ensemble and deep learning methods | |
16) | Final |
Course Notes / Textbooks: | Okutman Notları |
References: | Lecture Notes |
Ders Öğrenme Kazanımları | 1 |
2 |
3 |
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Program Outcomes | ||||||||||||||||
1) PO 1.1) Sufficient knowledge in mathematics, science and computer engineering | ||||||||||||||||
2) PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems. | ||||||||||||||||
3) PO 2.1) Identifying complex engineering problems | ||||||||||||||||
4) PO 2.2) Defining complex engineering problems | ||||||||||||||||
5) PO 2.3) Formulating complex engineering problems | ||||||||||||||||
6) PO 2.4) Ability to solve complex engineering problems | ||||||||||||||||
7) PO 2.5) Ability to choose and apply appropriate analysis and modeling methods | ||||||||||||||||
8) PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions. | ||||||||||||||||
9) PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product | ||||||||||||||||
10) PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | ||||||||||||||||
11) PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | ||||||||||||||||
12) PO 4.3) Ability to use information technologies effectively. | ||||||||||||||||
13) PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments | ||||||||||||||||
14) PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation | ||||||||||||||||
15) PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection | ||||||||||||||||
16) PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics | ||||||||||||||||
17) PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | PO 1.1) Sufficient knowledge in mathematics, science and computer engineering | |
2) | PO 1.2) Ability to apply theoretical and applied knowledge in mathematics, science and computer engineering for modeling and solving engineering problems. | |
3) | PO 2.1) Identifying complex engineering problems | |
4) | PO 2.2) Defining complex engineering problems | |
5) | PO 2.3) Formulating complex engineering problems | |
6) | PO 2.4) Ability to solve complex engineering problems | |
7) | PO 2.5) Ability to choose and apply appropriate analysis and modeling methods | |
8) | PO 3.1) Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions. | |
9) | PO 3.2) Ability to apply modern design methods under realistic constraints and conditions for a complex system, process, device or product | |
10) | PO 4.1) Developing modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | |
11) | PO 4.2) Ability to select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in engineering applications | 5 |
12) | PO 4.3) Ability to use information technologies effectively. | |
13) | PO 5.1) Examination of complex engineering problems or discipline-specific research topics, designing experiments | |
14) | PO 5.2) Examination of complex engineering problems or discipline-specific research topics, experimentation | |
15) | PO 5.3 ) Analysis of complex engineering problems or discipline-specific research topics, data collection | |
16) | PO 5.4) Analyzing the results of complex engineering problems or discipline-specific research topics | |
17) | PO 5.5) Examining and interpreting complex engineering problems or discipline-specific research topics |
Homework |
Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama) | |
Homework |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 10 | % 10 |
Homework Assignments | 2 | % 20 |
Midterms | 1 | % 30 |
Semester Final Exam | 1 | % 40 |
total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Study Hours Out of Class | 35 | 3 | 105 |
Midterms | 1 | 2 | 2 |
Final | 1 | 3 | 3 |
Total Workload | 152 |