EI412: Machine Vision and Application in Industry

Announcements

• Please print and bring lecture notes before attending class

Course Information

Description: Computer acquisition and analysis of image data with emphasis on techniques for robot vision. Image formation and image sensing. Image segmentation. Edge detection. Shape finding. Pattern recognition. Automated machine vision applied to assembly and inspection tasks in industry. This course is conducted using projected-based learning approach.

Course staff and contact information:

Dr. Songyot Nakariyakul
E-mail: nsongyot@engr.tu.ac.th

Problem sets: You need to do problem sets to reinforce what you learn in lecture. Copying other people's work is strictly prohibited.

Grading: Your grade in this course will be based principly on attendance, problem sets and exams as follows.

Attendance 10%
Problem sets 10%
Midterm exam 30%
Project 50%

Software: You will need MATLAB to implement some examples in class and to do your homework. If you are not familiar with MATLAB, please refer to the MATLAB and Image Processing primer provided at www.fundipbook.com

Image Viewer: free software www.xnview.com

Textbooks & Materials:

1. Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 4th ed, Pearson Prentice Hall, 2018.
2. E.R. Davies, Computer & Machine Vision: Theory, Algorithms, Practicalities, 4th ed, Academic Press, 2012.
3. Chris Solomon and Toby Breckon, Fundamentals of Digital Image Processing: a Practical Approach with Examples in MATLAB, John Wiley & Sons, 2011 (optional)

Handouts