LE415: Digital Image Processing

Announcements

Course Information

Description: Historical development of image processing. Image data structures. Image Preprocessing. Image enhance ment. Image classification. Image postprocessing. Image compression and restoration. Figure modeling. Computer animation. Contour mesh conversion. Applications of image processing. Introduction to computer vision.

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).

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
HW3
Sol2
Sol3
V Frequency-domain Processing G&W ch. 4 slides HW4 Sol4
VI Image Restoration G&W ch. 5.1-5.9 slides    
VII Color Image Processing G&W ch. 6 slides    
VIII Morphological Processing G&W ch. 9.1-9.5 slides HW5 Sol5
IX Image Segmentation G&W ch. 10.1-10.4 slides1
slides2
HW6 Sol6
X Object Recognition G&W ch. 12 slides
   
XI Geometry Solomon&Breckon ch. 7

slides
Material