BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| MECHATRONICS ENGINEERING (ENGLISH, NONTHESIS) | |||||
| 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 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Basic information
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| 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. |
Learning Outcomes
|
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 / 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/ |
Evaluation System
| 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 | |
ECTS / Workload Table
| 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 | ||
Contribution of Learning Outcomes to Programme Outcomes
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |