UAT 483 · 3(1-4-4) · Year/term 4/1

Computer Vision Applications

การประยุกต์ใช้การมองเห็นด้วยคอมพิวเตอร์

Progress

Notional hours: 135 h (online/self-study 60 · in class/lab 75)

Course description

image and video processing, edge and feature detection, image matching, object detection, classification, segmentation, tracking, image-based measurement, and development of applications for inspection, navigation, surveying, and intelligent systems

Thai description

การประมวลผลภาพและวิดีโอ การตรวจจับขอบและคุณลักษณะ การจับคู่ภาพ การตรวจจับวัตถุ การจำแนก การแบ่งส่วน การติดตาม การวัดจากภาพ และการพัฒนางานประยุกต์ด้านตรวจสอบ นำร่อง สำรวจ และระบบอัจฉริยะ

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 492

Course learning outcomes (CLO)

CLO	Outcome	PLO
CLO1	Select computer vision techniques for problems	PLO4
CLO2	Develop drone image-processing applications	PLO4PLO5
CLO3	Evaluate and improve application performance	PLO4

Learning modules

1CV tasks for drones
Weeks 1–3 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

Computer vision and the path to edge AITH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

2OpenCV
Weeks 4–6 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

OpenCVTH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

3Detection and segmentation
Weeks 7–9 · 27 h2 KU

Online (before class) · 12 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

Learning evidence: Checked worksheets and quiz results

4GeoAI
Weeks 10–12 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

GeoAI and publishing mapsTH

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

5Evaluation and deployment
Weeks 13–15 · 27 h2 KU

Online (before class) · 12 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

Learning evidence: Checked worksheets and quiz results

Assessment (draft)

Labs and worksheets	35%
Module quizzes	10%
Midterm examination	20%
Mini-project or practical exam	35%

Knowledge domain

D11 Artificial intelligence and computer vision D13 Surveying, mapping and geoinformatics

Key references

Szeliski, R. (2022). Computer vision: Algorithms and applications (2nd ed.). Springer. link
OpenCV. OpenCV documentation. link