Computer Vision and Perception Technology
การมองเห็นด้วยคอมพิวเตอร์และเทคโนโลยีการรับรู้
Course description
digital image processing, image enhancement, feature extraction, object detection and classification, image segmentation, object tracking, depth estimation, environmental perception, sensor fusion, and applications to UAS missions
Thai description
การประมวลผลภาพดิจิทัล การปรับปรุงภาพ การสกัดคุณลักษณะ การตรวจจับและจำแนกวัตถุ การแบ่งส่วนภาพ การติดตามวัตถุ การประมาณความลึก การรับรู้สภาพแวดล้อม การรวมข้อมูลจากเซนเซอร์ และการประยุกต์กับภารกิจอากาศยานไร้คนขับ
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 · 27 hLesson 85 minquiz2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
2Camera and IMU calibration
Weeks 4–6 · 27 hLesson 90 minquiz2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
3Object detection and tracking
Weeks 7–9 · 27 hLesson 90 minquiz2 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 · 12 h
Lab or field practice from worksheets with a safety checklist
4VIO and SLAM
Weeks 10–12 · 27 hLesson 90 minquiz2 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 h
Lab or field practice from worksheets with a safety checklist
5Evaluating perception reliability
Weeks 13–15 · 27 hLesson 85 minquiz3 KU
Online (before class) · 15 h
Study the assigned knowledge units in advance, review media and take the module quiz
In class / field · 12 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. link
- Corke, P. (2023). Robotics, vision and control: Fundamental algorithms in Python. Springer. link