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
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.
Course Description The students who succeeded in this course;
  • will be able to describe theoretical and practical aspects of signal processing using images,
  • will be able to explain the principles of image formation and analysis,
  • will be able to discuss main technical approaches in computer vision,
  • will be able to express basics of measurement and robust detection of features in images,
  • will be able to describe various methods used for registration, alignment, and matching of images.
Course Content 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.

 



Course Category

Core Courses
Major Area Courses
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 Image-based Rendering Chapter 13. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
15 Recognition Chapter 14. Szeliski, Richard. Computer vision: algorithms and applications. Springer Science & Business Media, 2010.
16 Project presentations

 

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.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
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
Study Hours Out of Class
14
2
Field Work
Quizzes / Studio Critiques
Homework / Assignments
Presentation / Jury
Project
1
30
Seminar / Workshop
Oral Exam
Midterms
1
20
Final Exam
1
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, Computer Science and Software 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 Software Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.

X
3

To be able to design, implement, verify, validate, document, measure and maintain a complex software system, process, or product under realistic constraints and conditions, in such a way as to meet the requirements; ability to apply modern methods for this purpose.

X
4

To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in software 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 Software Engineering problems.

X
6

To be able to work effectively in Software Engineering disciplinary and multi-disciplinary teams; to be able to work individually.

X
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 be able to present effectively, to be able to give and receive clear and comprehensible instructions.

X
8

To have knowledge about global and social impact of engineering practices and software applications on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Engineering and Software Engineering solutions.

X
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.

X
11

To be able to collect data in the area of Software Engineering, and to be able to communicate with colleagues in a foreign language.

X
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 Software Engineering.

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest