CYBER SECURITY (ENGLISH, THESIS)
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 7
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

Sources

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 - https://www.ceemple.com/

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
PERCENTAGE OF SEMESTER WORK % 50
PERCENTAGE OF FINAL WORK % 50
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) Being able to develop and deepen their knowledge at the level of expertise in the same or a different field, based on undergraduate level qualifications.
1) To be able to supervise and teach these values by observing social, scientific, cultural and ethical values in the stages of collecting, interpreting, applying and announcing the data related to the field.
1) Being able to independently carry out a work that requires expertise in the field.
1) To be able to critically evaluate the knowledge and skills acquired in the field of expertise and to direct their learning.
1) To be able to systematically transfer current developments in the field and their own studies to groups in and outside the field, in written, verbal and visual forms, by supporting them with quantitative and qualitative data.
2) To be able to interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines,
2) To be able to develop strategy, policy and implementation plans in the fields related to the field and to evaluate the obtained results within the framework of quality processes.
2) To be able to critically examine social relations and the norms that guide these relations, to develop them and take action to change them when necessary.
2) To be able to use the theoretical and applied knowledge at the level of expertise acquired in the field.
2) To be able to develop new strategic approaches for the solution of complex and unpredictable problems encountered in applications related to the field and to produce solutions by taking responsibility.
2) To be able to comprehend the interdisciplinary interaction with which the field is related.
3) To be able to use the knowledge, problem solving and/or application skills they have internalized in their field in interdisciplinary studies.
3) Being able to lead in environments that require the resolution of problems related to the field.
3) To be able to solve the problems encountered in the field by using research methods.