SDST3010 Image processing and computer vision (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 15
Course Co-ordinator Prof K Han, SCDS (Department of Statistics and Actuarial Science) < kaihanx@hku.hk >
Teachers Involved (Prof K Han,Statistics & Actuarial Science)
Course Objectives The course introduces the fundamentals of image processing and computer vision, covering both theoretical and computational aspects of the subject. On the theoretical aspect, the course introduces mathematical foundations for image processing and computer vision including representation of digital images, image processing techniques, feature detection and extraction, imaging models, stereo vision, image recognition and beyond. On the computational side, algorithms and their implementation are emphasized during the lectures and exercised during tutorials.
Course Contents & Topics Course content includes the following topics
- Imaging systems and representation of digital images;
- Image transformation and filtering;
- Image resolutions, sub-sampling, interpolation, and color models;
- Feature detection and description;
- Perspective projection and camera models;
- Camera calibration;
- Stereo vision;
- Deep learning for image recognition and beyond.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 understand the theoretical foundations of image formation, transformation, and filtering
CLO 2 understand the theoretical foundations of feature extraction, camera projection, stereo vision, and image recognition
CLO 3 design and implement various algorithms for digital image processing and computer vision
CLO 4 achieve simple image processing and computer vision tasks on real-world visual data
CLO 5 acquire hands-on experience in the use of image processing and computer vision tools
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in (MATH2014 or MATH2101 or SDST2602) and (COMP2113 or COMP2119 or COMP2396); and
Not for students who have passed in APAI3010, or already enrolled in this course.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Major in Decision Analytics ( Disciplinary Elective )
2023 Major in Decision Analytics ( Disciplinary Elective )
2022 Major in Decision Analytics ( Disciplinary Elective )
2021 Major in Decision Analytics ( Disciplinary Elective )
Course to PLO Mapping 2024 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2023 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2022 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
2021 Major in Decision Analytics < PLO 1,2,3,4,5,6 >
Offer in 2025 - 2026 Y        2nd sem    Examination May     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, class test(s) and a group project) 50.0 1,2,3,4,5
Examination One 2-hour written examination 50.0 1,2,3
Required/recommended reading
and online materials		 David Forsyth and Jean Ponce (2012), Computer Vision: A Modern Approach (2nd ed.), Pearson
Richard Szeliski (2022), Computer Vision: Algorithms and Applications (2nd ed., PDF available online), Springer Science & Business Media
Richard Hartley and Andrew Zisserman (2004), Multiple View Geometry in Computer Vision (2nd ed.), Cambridge University Press
Course Website http://moodle.hku.hk
Additional Course Information