| 課程與核心能力關聯配比(%) |
課程目標之教學方法與評量方法 |
| 課程目標 |
核心能力 |
配比(%) |
教學方法 |
評量方法 |
| The course introduces fundamental principles related to computer graphics, computer vision, other image-related technologies, and their applications in various fields. Through this course, students will acquire the capability to solve image processing and recognition problems. This course will demonstrate these theories and techniques with Python and Matlab implementations. |
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| 授課內容(單元名稱與內容、習作/每週授課、考試進度-共18週) |
| 週次 |
授課內容 |
| 第1週 |
Introduction to image processing, computer graphics and computer vision |
| 第2週 |
Overview of Python & Matlab, mathematical fundamentals |
| 第3週 |
Digital image fundamentals (I) |
| 第4週 |
Digital image fundamentals (II) |
| 第5週 |
Image enhancement in spatial domain |
| 第6週 |
Image enhancement in frequency domain |
| 第7週 |
Image restoration |
| 第8週 |
Mid-term Report |
| 第9週 |
Feature detection (I) |
| 第10週 |
Feature detection (II) |
| 第11週 |
Image segmentation |
| 第12週 |
Image recognition (I) |
| 第13週 |
Image recognition (II) |
| 第14週 |
Deep learning in computer vision (I) |
| 第15週 |
Deep learning in computer vision (II) |
| 第16週 |
Final-Project Presentation |
| 第17週 |
Self-directed Learning I - Participate in relevant lectures, workshops, or seminars using materials designated by the instructors. |
| 第18週 |
Self-directed Learning II - Participate in relevant lectures, workshops, or seminars using materials designated by the instructors. |
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| 學習評量方式 |
| Mid-term Report (30%), Final Project Present (40%), Homework/Participation (30%) |
| 教科書＆參考書目(書名、作者、書局、代理商、說明) |
Main Textbook: Handout
● Richard Szeliski (2022) Computer Vision: Algorithms and Applications (2/e), Springer, ISBN:3030343715.
● Rafael C. Gonzalez and Richard E. Woods (2017) Digital Image Processing (4/e), Pearson FT Press, ISBN:1292223049. |
| 課程教材（教師個人網址請列在本校內之網址） |
| The course materials would be found at ilearn. |
| 課程輔導時間 |
| Thursday 9:00-10:00 am or please contact the teacher to arrange a meeting time. |
| 聯合國全球永續發展目標(連結網址) |
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