Faculty of Engineering and Natural Sciences · Computer Engineering · Undergraduate
ECTS: 5 T+P+L: 2+0+1 Departmental Elective
Coordinator: Dr. Öğr. Üyesi Hasibe Büşra AYTEKİN
Instructors: Dr. Öğr. Üyesi Hasibe Büşra AYTEKİN
Course Objective
The goal of machine vision is to calculate properties of the three-dimensional world from digital images. Problems in this area include identifying the 3D shape of an environment through analysis of images and videos, determining how objects move, and recognizing familiar people and objects.
Course Content
Continuing with low-level image perception features such as image formation, cameras, color, and mid-level vision topics such as interest point detection and local feature extraction, it presents the foundations of higher-level vision tasks such as face detection/recognition, object recognition, and human motion analysis.
Course Learning Outcomes
- Examine stereo vision techniques and optical flow methods for studying motion.
- Solve basic computer vision problems using programs such as MATLAB, Python, OpenCV.
- Apply morphological operations for shape recognition and template matching.
- Use advanced algorithms such as support vector machines and artificial neural networks for object recognition and classification.
Core Area Distribution
(52) Engineering and Engineering Trades%100
Teaching Methods
ExpressionQuestion-AnswerExercise and PracticeSelf studyProblem Solving
Assessment & Evaluation
HomeworkProject / DesignTesting (Essay / Tests: True-Falls, multiple-choice, short answer, matching)
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 16 | 3 | 48 |
| Out of Class Study Period | 16 | 3 | 48 |
| Midterm | 1 | 35 | 35 |
| Quiz | 0 | 0 | 0 |
| Assignment | 2 | 15 | 30 |
| Practice | 0 | 0 | 0 |
| Final | 1 | 50 | 50 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction to machine vision | Lecture Notes |
| 2 | Basic concepts | Lecture Notes |
| 3 | Image Processing | Lecture Notes |
| 4 | Fillers and edge detection | Lecture Notes |
| 5 | Filters and edge detection | Lecture Notes |
| 6 | Features | Lecture Notes |
| 7 | Features | Lecture Notes |
| 8 | Midterm Exam | Lecture Notes |
| 9 | Camera Calibration | Lecture Notes |
| 10 | Stereo Vision | Lecture Notes |
| 11 | Stereo vision | Lecture Notes |
| 12 | Motion | Lecture Notes |
| 13 | Motion | Lecture Notes |
| 14 | Object Modeling and Recognition | Lecture Notes |
| 15 | Object Modeling and Recognition | Lecture Notes |
| 16 | Final | Lecture Notes |