icemansina/cuhksz_dip

This repository is mainly used for CUHKSZ course EIE4512: Digital Image Processing

On course tutorial

Supplemental texts (Optional):

• Digital Image Processing (3rd Edition) by Rafael C. Gonzalez and Richard E. Woods, Prentice-Hall

• Digital Image Processing Using MATLAB, 2nd ed. by Rafael C. Gonzalez, Richard E. Woods and Steven L. Eddins Gatemark Publishing

• Introduction to Digital Image Processing with MATLAB by Alasdair McAndrew, Course Technology

• Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab by Chris Solomon and Toby Breckon, Wiley

Software Utilities

Your programs will be written in Matlab or Python, which are installed on the your own machines.

Grade Policy

30% assignments (10% * 3), 30% exam (one mid-term exam), 40% final project (presentation + paper writing)

Late Assignment Credit: Late programming assignments will be penalized 15 percent per day (per 24 hours). Assignments later than 4 days late will not be accepted.

Notes:

• Reading is mandatory, working ahead is encouraged.

• Exams shall be based on lectures, readings and a bit of project knowledge, so class attendance is strongly encouraged, but not recorded.

• Working and discussions in pairs is okay. However, each student must turn in different and unique projects.

• Cheating is strictly forbidden

  • Cheating (a.k.a., academic dishonesty), defined as taking credit for work you did not do or knowledge you do not possess, is strictly forbidden. First offenders will receive a zero grade for the assignment or exam in question and an academic dishonesty report will be filed with the Office of Student Affairs. Repeat offenders will receive an F for the course and the case will be brought before the campus hearing board (see Student Handbook).

• All assignments should be submitted electronically. Hard copies or submissions on disks will not be accepted. Both your executable and source code must be turned in. Your documentation MUST include the structure of your project, what each file contains and instructions for compiling and running the program. Typically, a well-organized README ASCII text file is sufficient. Insufficient documentation will result in a loss of points. Data files should include a comment line at the start giving your name, the assignment for which it is intended, and the most recent date in which the file was changed. Please do NOT turn in hardcopies!! Your README file should be ASCII text, Microsoft Word or PDF.

Table of contents

Week 1: Introduction of digital image processing

Week 2: Intensity Transformation and histogram equalization

Week 3: Histogram operations and Spatial filtering

Week 4: Frequency Domain Operation

Week 5: Image Restoration_Reconstruction

Week 6: Edge Detection

Week 7: Optical flow

Week 8: Morphological Filters

Week 9: HOG and Object Detection

Week 10: Final Project proposal presentation and object detection

Week 11: Geometric operations, image warp and image registration

Week 12: Image Compression

Week 13: Basic Image Segmentation

Week 14: Advanced Image Segmentation