CYBER SECURITY (ENGLISH, NONTHESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
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 |
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. |
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 |
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 - https://www.ceemple.com/ |
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 |
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 |
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) | Being able to independently carry out a work that requires expertise in the field. | |
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) | 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 interpret and create new knowledge by integrating the knowledge gained in the field with the knowledge from different disciplines, | |
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 comprehend the interdisciplinary interaction with which the field is related. | |
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 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 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 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. | |
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. |