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課程名稱 |
粒子流動影像分析
Particle Flow Imaging |
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開課學期 |
100-1 |
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授課對象 |
工學院 水利工程組 |
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授課教師 |
卡艾瑋 |
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課號 |
CIE7101 |
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課程識別碼 |
521 M6510 |
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班次 |
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學分 |
3 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期二2,3,4(9:10~12:10) |
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上課地點 |
土研407 |
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備註 |
本課程中文授課,使用英文教科書。
總人數上限：34人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
This is a training course covering digital imaging methods for fluid and particulate flow measurements. Imaging techniques offer some of the most powerful means to visualise and characterise flow fields encountered in laboratory, industrial and field applications. The objective is for students to acquire the background, tools and first-hand experience necessary to apply imaging methods to their own projects in a scientific or professional context. The course will involve two components of equal importance:
1) class lectures aimed at introducing the principles and algorithms, complemented by targeted exercises (both in-class tasks and homework assignments);
2) project-based learning in which students (on their own or in groups of two students develop an application of their own design, possibly related to their PhD or MSc thesis.
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課程目標 |
Aspects covered in the lectures will include:
1. Scene preparation, imaging using digital cameras, and basic image processing
2. Motion analysis in the 2D plane: particle tracking velocimetry (PTV)
3. Camera calibration and ray intersection in 3D: stereo matching and 3D PTV
4. Contour and surface capture
5. Flow field analysis: particle trajectories and coherent motions.
Applications used as examples will include: turbulent flows seeded with tracers; granular flows; free-surface flows; biological, bulk handling, and traffic applications.
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課程要求 |
Students will be challenged to develop projects of their own design. In order to keep the time investment within reasonable limits, two types of projects will be encouraged:
1) Small-scale tabletop applications set up by students in the laboratory, followed by imaging analysis of the acquired camera footage using available methods (from the lectures and toolbox). The emphasis would then be placed on designing and analysing a simple experiment using imaging techniques.
2) Image analysis case study involving the development of special algorithms (implementing principles proposed in the literature) and applied to available footage (image sequences provided by the instructor, obtained from other investigators, or downloaded from the web). The emphasis here would be on designing and applying image or flow analysis methods.
The aim of the projects is not to develop sophisticated techniques and applications, but to encourage students to set up and solve an original problem using imaging methods. Progress and final results will be reported in interim reports and a final presentation.
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
Supporting material for the course will include three components:
1) a set of lecture notes (in English) introducing principles and algorithms;
2) a toolbox of existing digital imaging methods implemented in Matlab, providing basic tools for students to work from;
3) an archive of digital footage serving as raw material for students to experiment with.
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參考書目 |
Jahne, B. (1995) Digital Image Processing. Springer.
Jain, R., Kasturi, R., and Schunck, B.G. (1995) Machine Vision. McGraw-Hill.
Shapiro, L.G., and Stockman, G.C. (2001) Computer Vision. Prentice Hall.
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評量方式
(僅供參考) |
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