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 / Workshop
Lecture / 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;
  • 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 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.

 



Course Category

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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