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:
- Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd ed, Pearson Prentice Hall, 2010.
- Chris Solomon and Toby Breckon, Fundamentals of Digital Image Processing: a Practical Approach with Examples in MATLAB, John Wiley & Sons, 2011 (optional).
- Richard Szeliski, Computer Vision: Algorithms and Applications, Springer-Verlag, London, 2011 (optional)
|Topic||Reading||Lecture Notes||Problem Set||Solution|
|I||Introduction||G&W ch. 1||slides|
|II||Digital Image Fundamentals||G&W ch. 2||slides|
|IV||Spatial Enhancement||G&W ch. 3||slides1
|V||Frequency-domain Processing||G&W ch. 4||slides||HW2||Sol2|
|VI||Image Restoration||G&W ch. 5.1-5.9||slides||HW3||Sol3|
|VII||Color Image Processing||G&W ch. 6||slides|
|VIII||Morphological Processing||G&W ch. 9.1-9.5||slides||HW4||Sol4|
|IX||Image Segmentation||G&W ch. 10.1-10.4||slides1
|X||Object Recognition||G&W ch. 12||slides
|XI||Automated Visual Inspection||slides|