Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP5550 Computer Vision Fall 3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

Language of instruction: En
Type of course: Departmental Elective
Course Level:
Mode of Delivery: Face to face
Course Coordinator : Dr. Öğr. Üyesi TARKAN AYDIN
Course Lecturer(s): Dr. Öğr. Üyesi TARKAN AYDIN
Course Objectives: This class introduces the fundamental techniques in computer vision. Initially basic concepts of image formation, representation and camera projection geometries will be given. Later some classical image processing techniques will be introduced such as edge detection, segmentation, thresholding etc. Image matching, optical flow, local image features will be described in the context of multiple image processing. Basic image recognition techniques are also to be introduced. 3D inference will be another focus where stereo imaging, 3D reconstruction and various shape from X techniques are to be discussed.

Learning Outputs

The students who have succeeded in this course;
Upon successful completion of this course, students will be able to:
1- List the main components of the computer vision processes
2- Identify the latest development of Computer Vision
3- Apply 3D vision techniques in real world applications

Course Content

Weekly Detailed Course Contents

Week Subject Related Preparation
1) Fundamental Concepts
2) Digital Image processing techniques
3) Edge Detection
4) Line and curve detection
5) Camera Calibration
6) Stereo Vision
7) Image segmentation
8) Optical flow
9) Analysis of visual Motion
10) Shape from focus-defocus
11) Shape From Motion
12) Shape From Motion
13) Object detection and Recognition
14) Object detection and Recognition


Course Notes: "Computer Vision: Algorithms and Applications", Richard Szeliski "Introductory Techniques for 3-D Computer Vision", Trucco and Verri "Computer vision: A Modern Approach," David A. Forsyth, Jean Ponce • “Machine Vision” by Ramesh Jain, Rangachar Kasturi, Brian G. Schunck
References: Ceemple OpenCV IDE -

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Attendance % 0
Laboratory % 0
Application % 0
Field Work % 0
Special Course Internship (Work Placement) % 0
Quizzes % 0
Homework Assignments 3 % 30
Presentation % 0
Project 1 % 20
Seminar % 0
Midterms 1 % 20
Preliminary Jury % 0
Final 1 % 30
Paper Submission % 0
Jury % 0
Bütünleme % 0
Total % 100
Total % 100

ECTS / Workload Table

Activities Number of Activities Duration (Hours) Workload
Course Hours 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 14 2 28
Presentations / Seminar 0 0 0
Project 1 30 30
Homework Assignments 4 12 48
Quizzes 0 0 0
Preliminary Jury 0
Midterms 1 20 20
Paper Submission 0
Jury 0
Final 1 30 30
Total Workload 198

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) Understand and implement advanced concepts of Siber Security
2) Use math, science, and modern engineering tools to formulate and solve advenced siber security problems.
3) Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
4) Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
5) Work effectively in multi-disciplinary research teams
6) Acquire scientific knowledge
7) Find out new methods to improve his/her knowledge.
8) Effectively express his/her research ideas and findings both orally and in writing
9) Defend research outcomes at seminars and conferences.
10) Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc.
11) Demonstrate professional and ethical responsibility.