Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Fall Semester
Year 2020 280165 Computer Vision and Image Measurement 1 3.0 3 ★ Yuan-Sen Yang GCDPE
IDEOM 10 1 ◎

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Fall Semester Year 2020	280165	Computer Vision and Image Measurement	1	3.0	3	★	Yuan-Sen Yang	GCDPE IDEOM	10	1	◎

Course Plan：

Instructor Yuan-Sen Yang

Email ysyang@ntut.edu.tw

Last Updated 2020-06-12 17:16:32

Course Objective The rapid development and population of digital imaging hardware and algorithms bring high potential of computer vision and image measurement on practical applications. These applications can include but not limited to measurement, structural health monitoring, and safety identification. This course not only introduces the theory of computer vision and image measurement, but also guides students to operate and implement structural experiments and measurement projects. Students are encouraged to bring up the concepts and theories, programming skills, experimental design and implementation, oral presentations, as well as experimental documentation and technical writing ability.

Course Schedule 1. Overview: C++ basics. OpenCV installation. 2. Introduction to Computer Vision and OpenCV: OpenCV samples, 2D transformation. 3. General data types of images, matrices, and vectors: Row-wise format of matrix/vector. Channels of data. Memory operations in programming. 4. Basic vector and matrix operations. Matrix multiplication/inversion. Fourier transform. Normalization. 5. 2D coordinate transformation. Formula. Rotation. Stretch. Warping. 6. Image tracking. Template match. Optical flow. Enhanced correlation. 7. Data filtering and convolution. 8. 2D image analysis. Fourier transform. Edge detection. Hough transformation. 9. Midterm 10. 3D Coordinate transformation. 3-by-3 and 4-by-4 transformation matrix. 11. Camera model and calibration. 12. Point projection. 13. Final project introduction. 14. Final project proposal. 15. Special topic 1 16. Special topic 2 17. Special topic 3 18. Final project presentation.

Evaluation and grading policy Midterm 25% +/- 5% Assignments 25% +/- 5% Final Presentation 25%+/- 5% Final Report 25%+/- 5%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Kaehler, A.; Bradski, G. (2016). Learning OpenCV 3 Computer Vision in C++ with the OpenCV Library, O'Reilly. ISBN: 978-1-491-93799-0.

The access to curricular consultation

Expanding teaching and resources

The course corresponds to the SDGs

Does the course incorporate AI

Note
　

Instructor	Yuan-Sen Yang
Email	ysyang@ntut.edu.tw
Last Updated	2020-06-12 17:16:32
Course Objective	The rapid development and population of digital imaging hardware and algorithms bring high potential of computer vision and image measurement on practical applications. These applications can include but not limited to measurement, structural health monitoring, and safety identification. This course not only introduces the theory of computer vision and image measurement, but also guides students to operate and implement structural experiments and measurement projects. Students are encouraged to bring up the concepts and theories, programming skills, experimental design and implementation, oral presentations, as well as experimental documentation and technical writing ability.
Course Schedule	1. Overview: C++ basics. OpenCV installation. 2. Introduction to Computer Vision and OpenCV: OpenCV samples, 2D transformation. 3. General data types of images, matrices, and vectors: Row-wise format of matrix/vector. Channels of data. Memory operations in programming. 4. Basic vector and matrix operations. Matrix multiplication/inversion. Fourier transform. Normalization. 5. 2D coordinate transformation. Formula. Rotation. Stretch. Warping. 6. Image tracking. Template match. Optical flow. Enhanced correlation. 7. Data filtering and convolution. 8. 2D image analysis. Fourier transform. Edge detection. Hough transformation. 9. Midterm 10. 3D Coordinate transformation. 3-by-3 and 4-by-4 transformation matrix. 11. Camera model and calibration. 12. Point projection. 13. Final project introduction. 14. Final project proposal. 15. Special topic 1 16. Special topic 2 17. Special topic 3 18. Final project presentation.
Evaluation and grading policy	Midterm 25% +/- 5% Assignments 25% +/- 5% Final Presentation 25%+/- 5% Final Report 25%+/- 5%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Kaehler, A.; Bradski, G. (2016). Learning OpenCV 3 Computer Vision in C++ with the OpenCV Library, O'Reilly. ISBN: 978-1-491-93799-0.
The access to curricular consultation
Expanding teaching and resources
The course corresponds to the SDGs
Does the course incorporate AI
Note