Dirk Schnieders
Runmin Wu
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.
| 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 | |
| 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 |
| 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% | 8 - 20 August 2022 | CLO1 |
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* 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 available during the examination period. Absence from examination may result in a failure in the course. There is no supplementary examination for all MSc curriculums in the Faculty of Engineering. |
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- Available from the course web page
| Date | Time | Venue | Remark | |
| Session 1 | 7 Jun 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 2 | 14 Jun 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 3 | 21 Jun 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 4 | 28 Jun 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 5 | 5 Jul 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 6 | 12 Jul 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 7 | 19 Jul 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 8 | 26 Jul 2022 (Tue) | 7:00pm - 10:00pm | Online | Zoom |
| Session 9 | 30 Jul 2022 (Sat) | 2:00pm - 5:00pm | Online | Zoom |
| Session 10 | 30 Jul 2022 (Sat) | 7:00pm - 10:00pm | Online | Zoom |
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HKU Moodle:
https://moodle.hku.hk/course/view.php?id=89515
(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.