COMPUTER ENGINEERING (ENGLISH, NON-THESIS) | |||||
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 | 8 |
Language of instruction: | English |
Type of course: | Must Course |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Assist. Prof. TARKAN AYDIN |
Course Lecturer(s): |
Assist. Prof. TARKAN AYDIN |
Recommended Optional Program Components: | None |
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 / Textbooks: | "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 |
Homework Assignments | 3 | % 30 |
Project | 1 | % 20 |
Midterms | 1 | % 20 |
Final | 1 | % 30 |
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 |
Study Hours Out of Class | 14 | 2 | 28 |
Project | 1 | 30 | 30 |
Homework Assignments | 4 | 12 | 48 |
Midterms | 1 | 20 | 20 |
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) | Define and manipulate advanced concepts of Computer Engineering | 4 |
2) | Use math, science, and modern engineering tools to formulate and solve advenced engineering problems | 4 |
3) | Notice, detect, formulate and solve new engineering problems. | 4 |
4) | Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results | 2 |
5) | Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study | 3 |
6) | Work effectively in multi-disciplinary research teams | 3 |
7) | Acquire scientific knowledge | 3 |
8) | Find out new methods to improve his/her knowledge. | 3 |
9) | Effectively express his/her research ideas and findings both orally and in writing | 3 |
10) | Defend research outcomes at seminars and conferences. | 2 |
11) | Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc. | 1 |
12) | Demonstrate professional and ethical responsibility. | 2 |
13) | Develop awareness for new professional applications and ability to interpret them. | 2 |