COMP7502A - Image processing and computer vision

Summer Semester, 2024-25

Professor
Dirk Schnieders
Syllabus To study the theory and algorithms in image processing and computer vision.  Topics include image representation; image enhancement; image restoration; mathematical morphology; image compression; scene understanding and motion analysis.
Introduction by Professor This course will study both theory and applications of image processing and computer vision.

The first part of this course will cover digital image processing for the improvement of pictorial information for human and machine interpretation. Students will enhance and restore images in the spatial domain using convolution. The second part of this course will focus on computer vision, including stereo vision, feature extraction and deep learning with convolution neural networks for image classification.
Learning Outcomes
Course Learning Outcomes Relevant Programme Learning Outcome
CLO1. An understanding of the fundamental concepts and the mathematical tools used in digital image processing and computer vision PLO.5, 6, 7, 8, 9, 16
CLO2. Able to design and implement various algorithms for digital image processing and computer vision PLO.6, 7, 8, 9, 10, 11, 12
View Programme Learning Outcomes
Pre-requisites Good programming, knowledge of probability and linear algebra.
Compatibility Nil
Topics covered
Course Content No. of Hours Course Learning Outcomes
1. Introduction 3 CLO1
2. Digital Image Fundamentals 2 CLO1
3. Filtering and Feature Extraction 7 CLO1, CLO2
4. Camera Model and Stereo Vision 7 CLO1, CLO2
5. Convolutional Neural Networks 5 CLO1, CLO2
6. Project Presentations 6 CLO2
 
Assessment
Description Type Weighting * Examination Period ^ Course Learning Outcomes
Project Continuous Assessment 20% - CLO2
Lab Continuous Assessment 10%   CLO1, CLO2
Written Midterm Examination Continuous Assessment 20% - CLO1
Written exam covering all taught content of the course Written Examination 50% 4 - 16 Aug 2025 CLO1
* The weighting of coursework and examination marks is subject to approval
^ The exact examination date uses to be released when all enrolments are confirmed after add/drop period by the Examinations Office.  Students are obliged to follow the examination schedule.  Students should NOT enrol in the course if they are not certain that they will be in Hong Kong during the examination period.  Absent from examination may result in failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering.
Course materials Lecture Notes & Lab Instructions:
  • Available from the course web page
Session dates
Date Time Venue Remark
Session 1 9 Jun 2025 (Mon) 7:00pm - 10:00pm MW-T7  
Session 2 12 Jun 2025 (Thu) 7:00pm - 10:00pm MW-T7  
Session 3 16 Jun 2025 (Mon) 7:00pm - 10:00pm MW-T7  
Session 4 19 Jun 2025 (Thu) 7:00pm - 10:00pm MW-T7  
Session 5 23 Jun 2025 (Mon) 7:00pm - 10:00pm MW-T7  
Session 6 24 Jul 2025 (Thu) 7:00pm - 10:00pm MW-T7  
Session 7 28 Jul 2025 (Mon) 7:00pm - 10:00pm MW-T7  
Session 8 31 Jul 2025 (Thu) 7:00pm - 10:00pm MW-T7  
Session 9 2 Aug 2025 (Sat) 10:00am - 1:00pm MW-T7  
Session 10 2 Aug 2025 (Sat) 2:00pm - 5:00pm MW-T7  
MW - Meng Wah Complex
Add/drop 9 June, 2025 - 14 June, 2025
Maximum class size 137
Moodle course website
  • HKU Moodle: https://moodle.hku.hk/course/view.php?id=123929 (Login using your HKU Portal UID and PIN)

    - Please note that the professor maintains and controls when to release the Moodle teaching website to students.
    - Enrolled students should visit the Moodle teaching website regularly for latest announcements, course materials, assignment submission, discussion forum, etc.
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