課程目標主要介紹電腦視覺應用情境與目的，瞭解影像處理的基本取像方式與特徵擷取技巧。近代利用深度學習的特徵擷取方取方式，讓視覺應用更具有不同切入點，透過模型學習的方式擷取關聯性的特徵值。其熱門的應用有物件偵測、物件追蹤以及物件辨識等。本課程將透過不同產業的實際案例，讓學生了解如何結合AI電腦影像處理的知識，整合實際硬體取像設備、現場工作環境等，來解決實際問題，達到培育學生具備結合理論、實作與應用能力的目的。The course objectives mainly introduce the application scenarios and purposes of computer vision, and understand the basic imaging methods and feature extraction techniques of image processing. In modern times, the feature extraction method of deep learning has been used to allow visual applications to have different entry points, and the relevant feature values are extracted through model learning. Its popular applications include object detection, object tracking, and object identification. This course will use actual cases from different industries to let students understand how to combine the knowledge of AI computer image processing, integrate actual hardware imaging equipment, on-site working environment, etc., to solve practical problems, so as to cultivate students with the ability to combine theory, practice and The purpose of applying capabilities.
自編教材
Self-edited teaching materials
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 作業作業 Homework |
50 | |
| 期末專題期末專題 Final topic |
40 | |
| 課堂表現課堂表現 Classroom performance |
10 |
