EI434: Machine Vision and Application in Industry

Announcements

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.

Course staff and contact information:

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 30%
Midterm exam 30%
Final exam 30%

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. Chris Solomon and Toby Breckon, Fundamentals of Digital Image Processing: a Practical Approach with Examples in MATLAB, John Wiley & Sons, 2011 (optional).
  3. Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag, London, 2011 (optional)

Handouts

  Topic Reading Lecture Notes Problem Set Solution
  Syllabus   pdf
I Introduction G&W ch. 1 slides
II Digital Image Fundamentals G&W ch. 2 slides    
III MATLAB Tutorial   slides HW1 Sol1
IV Spatial Enhancement G&W ch. 3 slides1
slides2
HW2 Sol2
V Image Restoration G&W ch. 5.1-5.9 slides HW3 Sol3
VI Color Image Processing G&W ch. 6 slides HW4 Sol4
VII Morphological Processing G&W ch. 9.1-9.5 slides HW5 Sol5
VIII Image Segmentation G&W ch. 10.1-10.4 slides1
slides2
HW6 Sol6
IX Corner Detection   slides    
X Object Recognition G&W ch. 12 slides
   
XI Automated Visual Inspection   slides