Computer Vision and Perception Technology
การมองเห็นด้วยคอมพิวเตอร์และระบบรับรู้
Course description
Image processing and analysis; object detection, classification and tracking; optical and thermal cameras; LiDAR; position and distance estimation; and environmental perception.
Thai description
การประมวลผลภาพ การตรวจจับ จำแนกและติดตามวัตถุ Optical/Thermal Cameras, LiDAR, Position and Distance Estimation และ Environmental Perception
Course learning outcomes (CLO)
| CLO | Outcome | PLO |
|---|---|---|
| CLO1 | Explain image formation and camera geometry | PLO1PLO4 |
| CLO2 | Process images and calibrate cameras | PLO4 |
| CLO3 | Apply perception for navigation and detection | PLO4PLO5 |
Learning modules
1Digital images and cameras
Weeks 1–3 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
2Camera and IMU calibration
Weeks 4–6 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
3Object detection and tracking
Weeks 7–9 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
- Intelligent video analyticsIn development
- Evaluating an object-detection modelTH
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
4VIO and SLAM
Weeks 10–12 · 30 h2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
5Evaluating perception reliability
Weeks 13–15 · 30 h3 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 15 h
Lab or field practice from worksheets with a safety checklist
Assessment (draft)
| Labs and worksheets | 35% |
| Module quizzes | 10% |
| Midterm examination | 20% |
| Mini-project or practical exam | 35% |
Knowledge domain
Key references
- Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer. link
- Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed.). Cambridge University Press.
- Corke, P. (2023). Robotics, vision and control (3rd ed.). Springer.