FACULTY OF ENGINEERING
Department of Computer Engineering
CE 466 | Course Introduction and Application Information
Course Name |
Computer Vision
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
CE 466
|
Fall/Spring
|
3
|
0
|
3
|
5
|
Prerequisites |
None
|
|||||
Course Language |
English
|
|||||
Course Type |
Elective
|
|||||
Course Level |
First Cycle
|
|||||
Mode of Delivery | - | |||||
Teaching Methods and Techniques of the Course | Application: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
Course Coordinator | ||||||
Course Lecturer(s) | - | |||||
Assistant(s) | - |
Course Objectives | This course is designed to introduce fundamental principles and applications of computer vision. During the course, the fundamental concepts of computer vision will be discussed, real-world applications of computer vision will be described, and students will participate in a project where they will apply computer vision algorithms. |
Learning Outcomes |
The students who succeeded in this course;
|
Course Description | The following topics will be included: image formation, image processing, feature detection and matching, segmentation, feature-based alignment, structure from motion, dense motion estimation, image stitching, computational photography, stereo correspondence, 3D reconstructions, image-based rendering, and recognition. |
|
Core Courses | |
Major Area Courses |
X
|
|
Supportive Courses | ||
Media and Management Skills Courses | ||
Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
Week | Subjects | Related Preparation |
1 | Introduction to Computer Vision | Chapter 1. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
2 | Image Formation | Chapter 2. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
3 | Image Processing | Chapter 3. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
4 | Feature Detection and Matching | Chapter 4. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
5 | Segmentation | Chapter 5. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
6 | Feature-Based Alignment | Chapter 6. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
7 | Structure From Motion | Chapter 7. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
8 | Dense Motion Estimation | Chapter 8. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
9 | Midterm exam | |
10 | Image Stitching | Chapter 9. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
11 | Computational Photography | Chapter 10. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
12 | Stereo Correspondence | Chapter 11. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
13 | 3D Reconstruction | Chapter 12. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
14 | Project Presentations | |
15 | Semester Review | |
16 | Final Exam |
Course Notes/Textbooks | Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010. |
Suggested Readings/Materials | Shapiro and Stockman, Computer Vision, Prentice-Hall, 2001; Deep Learning, by Goodfellow, Bengio, and Courville; Dictionary of Computer Vision and Image Processing, by Fisher et al. Deep Learning, by Goodfellow, Bengio, and Courville. ISBN: 978-0262035613; Dictionary of Computer Vision and Image Processing, by Fisher et al. ISBN: 978-1119941866 |
EVALUATION SYSTEM
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project |
1
|
30
|
Seminar / Workshop | ||
Oral Exams | ||
Midterm |
1
|
30
|
Final Exam |
1
|
40
|
Total |
Weighting of Semester Activities on the Final Grade |
2
|
60
|
Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
Total |
ECTS / WORKLOAD TABLE
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
Study Hours Out of Class |
14
|
2
|
28
|
Field Work |
0
|
||
Quizzes / Studio Critiques |
0
|
||
Portfolio |
0
|
||
Homework / Assignments |
0
|
||
Presentation / Jury |
0
|
||
Project |
1
|
30
|
30
|
Seminar / Workshop |
0
|
||
Oral Exam |
0
|
||
Midterms |
1
|
20
|
20
|
Final Exam |
1
|
24
|
24
|
Total |
150
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
1
|
2
|
3
|
4
|
5
|
||
1 | To have adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. |
X | ||||
2 | To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. |
X | ||||
3 | To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose. |
|||||
4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively. |
X | ||||
5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics. |
|||||
6 | To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually. |
|||||
7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions. |
|||||
8 | To have knowledge about global and social impact of Computer Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Computer Engineering solutions. |
|||||
9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. |
|||||
10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. |
|||||
11 | To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1) |
|||||
12 | To be able to speak a second foreign language at a medium level of fluency efficiently. |
|||||
13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Computer Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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