Course Syllabus

Course Information：

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2026	363360	Advanced Computer Vision	1	3.0	3	★	Huang Cheng-Ming	GEE	54	0

Course Plan：

Instructor	Huang Cheng-Ming　Office Hours
Email	cmhuang@ntut.edu.tw
Last Updated	2026-08-18 15:04:24
Course Objective	電腦視覺簡介及其應用 影像形成 偵測與配對 影像分割 以特徵為基礎的定位 運動推論結構 運動估計 影像縫合 計算攝影學 立體對應 三維重建 影像辨識
Course Schedule (Week 1-16)	Week 1. Introduction Week 2. Image Formation Week 3. Thresholding and Segmentation Week 4. Region Analysis Week 5. Statistical Pattern Recognition Week 6. Mathematical Morphological Processing Week 7. Conditioning and Labeling Week 8. Facet Model and Texture Analysis Week 9. Midterm Exam Week 10. Object Models Week 11. Detection and Matching Week 12. Segmentation Week 13. Feature-Based Alignment Week 14. Structure from Motion, Motion Estimation Week 15. Image Stitching, Stereo Correspondence, 3D Reconstruction Week 16. Recognition Week 17. Presentation of Final Project Week 18. Presentation of Final Project
Flexible Learning (Week 17-18)	Category ● 參與課程相關作業、作品、實驗、田野調查(Participation in course-related assignments, works, experiments, or field research) ● AI 輔助學習與工具應用(AI-assisted learning and tool application) Content 期末專題報告與結果分析 Hours 4 Learning Outcomes 繳交報告 Assessment Percentage 5%
Evaluation and grading policy	Homework (with C programming) 70% Midterm Exam (closed book) 10% Final Project 20%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes -Textbook Richard Szeliski, Computer Vision: Algorithms and Applications, 2010. -Reference D. A. Forsyth and John Ponce, Computer Vision: A Modern Approach, Pearson Education, 2003. Richard Hartley and Andrew Zisserman, Multiple View Geometry in Computer Vision, 2nd ed, Cambridge University Press, March 2004.
The access to curricular consultation	E-mail: cmhuang@mail.ntut.edu.tw 北科 i 學園PLUS https://istudy.ntut.edu.tw/
Expanding teaching and resources	● 參與式創新教學 (Participatory & Innovative Teaching) 強調學生參與與產出，導入翻轉、PBL/專題、設計思考或遊戲化等策略。 Emphasizes student engagement and outcomes by adopting strategies such as Flipped Classroom, PBL (Problem/Project- Based Learning), Design Thinking, or Gamification. ● 數位科技融入 (Digital Technology Integration) 運用生成式AI、VR/AR、IoT、IRS等新興科技輔助教學與評量。 Utilizes emerging technologies such as Generative AI, VR/AR, IoT, and IRS (Interactive Response Systems) to facilitate teaching and assessment. ● 數位教材資源應用 (Application of Digital Resources) 採用影音多媒體或具開放授權之資源（如OCW、開放教科書）作為主要或輔助教材。 Adopts multimedia or open-licensed resources (e.g., OCW, Open Textbooks) as primary or supplementary teaching materials.
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Does the course incorporate AI	● 簡單介紹AI的基本概念（Briefly introduce the basic concept of AI） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
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