Syllabus ( ELEC 463 )
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Basic information
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| Course title: |
Fundamentals of Image Processing |
| Course code: |
ELEC 463 |
| Lecturer: |
Assist. Prof. Köksal HOCAOĞLU
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| ECTS credits: |
6 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
4, Fall and Spring |
| Level of course: |
First Cycle (Undergraduate) |
| Type of course: |
Area Elective
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| Language of instruction: |
Turkish
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| Mode of delivery: |
Face to face
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| Pre- and co-requisites: |
ELEC 367, MAT 214, INF212, ELM 368 |
| Professional practice: |
No |
| Purpose of the course: |
The goal of this course is to introduce to students the basic theory and recent important topics related to digital image processing. |
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Learning outcomes
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Upon successful completion of this course, students will be able to:
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Develop image processing algorithms to enhance digital images.
Contribution to Program Outcomes
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Obtain basic knowledge of Electronics Engineering.
Method of assessment
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Laboratory exercise/exam
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Term paper
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Decide which method is appropriate to tackle a given image processing problem.
Contribution to Program Outcomes
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Formulate and solve engineering problems
Method of assessment
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Laboratory exercise/exam
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Term paper
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Develop image processing algorithms for automatic target detection applications
Contribution to Program Outcomes
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Formulate and solve engineering problems
Method of assessment
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Term paper
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Identify and exploit analogies between the mathematical tools used for 1D and 2D signal analysis and processing
Contribution to Program Outcomes
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Obtain basic knowledge of Electronics Engineering.
Method of assessment
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Written exam
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Homework assignment
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Analyze 2D signals in the frequency domain through the Fourier transform
Contribution to Program Outcomes
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Obtain basic knowledge of Electronics Engineering.
Method of assessment
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Homework assignment
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Laboratory exercise/exam
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Implement digital image processing methods in a programming language based on a given algorithmic description or theory
Contribution to Program Outcomes
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Formulate and solve engineering problems
Method of assessment
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Laboratory exercise/exam
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Term paper
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Contents
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| Week 1: |
Introduction to digital image processing |
| Week 2: |
Intensity transformations and spatial filtering |
| Week 3: |
Histogram processing |
| Week 4: |
Filtering in the frequency domain (Two-dimensional DFT and its properties) |
| Week 5: |
Filtering in the frequency domain (Designing two-dimensional filters) |
| Week 6: |
Geometric transformation |
| Week 7: |
Image segmentation (basic concepts) |
| Week 8: |
Image segmentation (thresholding) |
| Week 9: |
Midterm exam |
| Week 10: |
Image Analysis |
| Week 11: |
Morphological image processing (binary) |
| Week 12: |
Morphological image processing (morphological algorithms) |
| Week 13: |
Morphological image processing (gray-level) |
| Week 14: |
Presentation of term project to class |
| Week 15*: |
. |
| Week 16*: |
Final Exam |
| Textbooks and materials: |
Rafael C. Gonzalez, Richard E. Woods, “Digital Image Processing”, Third Edition, Prentice Hall, 2008. |
| Recommended readings: |
Gonzalez, Woods, and Eddins, “Digital Image Processing using MATLAB”, Second Edition, Gatesmark Publishing, 2009. |
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* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
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Assessment
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| Method of assessment |
Week number |
Weight (%) |
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| Mid-terms: |
8 |
15 |
| Other in-term studies: |
2,3,4,7,10,11 |
20 |
| Project: |
6,9 |
30 |
| Homework: |
5,8 |
5 |
| Quiz: |
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0 |
| Final exam: |
16 |
30 |
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Total weight: |
(%) |
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Workload
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| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
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| Courses (Face-to-face teaching): |
3 |
13 |
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| Own studies outside class: |
3 |
12 |
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| Practice, Recitation: |
3 |
6 |
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| Homework: |
4 |
2 |
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| Term project: |
20 |
1 |
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| Term project presentation: |
3 |
1 |
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| Quiz: |
0 |
0 |
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| Own study for mid-term exam: |
10 |
1 |
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| Mid-term: |
2 |
1 |
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| Personal studies for final exam: |
12 |
1 |
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| Final exam: |
2 |
1 |
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Total workload: |
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Total ECTS credits: |
* |
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* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
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