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課程名稱 |
機器人視覺
Robot Vision |
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開課學期 |
108-2 |
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授課對象 |
工學院 機械工程學研究所 |
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授課教師 |
黃漢邦 |
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課號 |
ME5043 |
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課程識別碼 |
522 U6180 |
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班次 |
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學分 |
3.0 |
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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 |
星期一2,3,4(9:10~12:10) |
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上課地點 |
工綜205 |
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備註 |
與林峻永合授
總人數上限：40人 |
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Ceiba 課程網頁 |
http://ceiba.ntu.edu.tw/1082ME5043_vision |
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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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為確保您我的權利,請尊重智慧財產權及不得非法影印
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課程概述 |
This class is designed for the graduate or junior/ senior engineering students. Students will learn the image processing, model-based vision, camera model, calibration, pose estimation, stereo vision, and neural network (and AI) for robot vision. |
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課程目標 |
Design of algorithms for robotic vision systems for automation, manufacturing, and the service industries, image processing, optics, illumination, and feature representation. |
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課程要求 |
Engineering Mathematics |
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預期每週課後學習時數 |
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Office Hours |
每週四 14:00~16:00
每週三 14:00~16:00
每週四 13:00~15:00
每週一 13:00~15:00 備註： Prof. Han Pang Huang, Mon and Thur. hanpang@ntu.edu.tw
Prof. Chun Yeon Lin, Wed and Thur. chunyeonlin@ntu.edu.tw |
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指定閱讀 |
Course notes |
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參考書目 |
1. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, 4th Edition, 2018.
2. R. Gonzalez, R. Woods, and S. Eddins, Digital Image Processing using Matlab, 2nd ed., Prentice Hall, 2009.
3. D. H. Ballard and C. M. Brown, Computer Vision, Prentice Hall. 1982.
4. B. K. P. Horn, Robot Vision, MIT Press. 1986.
5. N. Zuech, Applying Machine Vision, Wiley Interscience. 1988.
6. R. M. Haralick and L. G. Shapiro, Computer and Robot Vision, V1 & 2, Addison Wesley. 1992.
7. F. van der Heijden, Image Based Measurement Systems, John Wiley and Sons, 1995.
8. E. R. Davies, Computer and Machine Vision: Theory, Algorithm, & Practicalities, 4th ed., Acad. Press, 2012.
9. Linda G. Shapiro and George C. Stockman, Machine Vision, Prentice Hall, 2001.
10. D. A. Forsyth, and J. Ponce, Computer Vision: A Modern Approach, Prentice Hall. 2nd ed., 2011.
11. R. Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag, London, 2011. |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Homework and Experiments |
25% |
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2. |
1st Exam |
25% |
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3. |
2nd Exam |
25% |
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4. |
Final Term Project |
25% |
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週次 |
日期 |
單元主題 |
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第1週 |
3/02 |
Introduction to robot vision
Overview of Matlab
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第2週 |
3/09 |
Fourier transform
Random process
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第3週 |
3/16 |
Image formation and image processing |
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第4週 |
3/23 |
Image formation and image processing |
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第5週 |
3/30 |
Camera calibration
Overview of OpenCV
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第6週 |
4/06 |
1st Exam |
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第7週 |
4/13 |
Model-based vision I:
image degradation and restoration |
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第8週 |
4/20 |
Model-based vision II:
Hough transform, curvature method
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第9週 |
4/27 |
Model-based vision III:
Featuring matching and selection
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第10週 |
5/04 |
Biologically inspired vision:
Neural network
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第11週 |
5/11 |
Geometric methods I:
Camera calibration
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第12週 |
5/18 |
Geometric methods II:
Hand eye calibration, pose estimation
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第13週 |
5/25 |
Geometric methods III:
Stereo vision, color vision
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第14週 |
6/01 |
Image segmentation |
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第15週 |
6/08 |
2nd Exam |
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第16週 |
6/15 |
Term project presentation |
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