| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
|
| Learning Outcomes |
At the end of this course, the students;
- Know the fundamental concepts of computer vision.
- Know the image formation and representation.
- Learn the segmentation, feature extraction, contour and region analysis along with the camera geometry and calibration.
- Comprehend the object and scene recognition.
- Have an opinion about the human activity recognition and inference.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | BIL 421 - Image Processing |
|---|
| Course Description | Fundamental concepts of computer vision. Visual perception of human being. Mathematical foundations. Image formation and representation. Segmentation, feature extraction, contour and region analysis, camera geometry and calibration. Motion capture. 3-D reconstruction. Object and scene recognition. Object and people tracking. Human activity recognition and inference. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Fundamental concepts of computer vision. |
|
2nd Week |
Visual perception of human being. |
|
3rd Week |
Mathematical foundations. |
|
4th Week |
Image formation and representation. |
|
5th Week |
Segmentation, feature extraction, contour and region analysis, camera geometry and calibration. |
|
6th Week |
Motion capture. |
|
7th Week |
3-D reconstruction. |
|
8th Week |
Mid-term |
|
9th Week |
Object and scene recognition. |
|
10th Week |
Object and people tracking. |
|
11th Week |
Human activity recognition and inference. |
|
12th Week |
Human activity recognition and inference. |
|
13th Week |
Human activity recognition and inference. |
|
14th Week |
Human activity recognition and inference. |
|
15th Week |
General Repeat. |
|
| Recommended or Required Reading | 1. Szeliski Richard, Computer Vision: Algorithms and Applications, ISBN: 9781848829343, Springer, 2010.
2. Parker J.R., Algorithms for Image Processing and Computer Vision, 2/E, ISBN: 0470643854, Wiley, 2010.
|
|---|
| Planned Learning Activities and Teaching Methods |
- Questions/Answers
- Lecture
- Problem Solving
- Experiment
- Project
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
4 |
10 |
| Project |
3 |
20 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1.5 |
1.5 |
| Preparation for Quiz |
4 |
1 |
4 |
| Individual or group work |
|
|
|
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
14 |
4 |
56 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
3 |
10 |
30 |
| Quiz |
4 |
0.5 |
2 |
| Total Work Load | 145.5 |
| Total Work Load / 30 (h) | 4.85 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
