| Course code | ENGG5104 |
| Course title | Image Processing and Computer Vision 圖像處理及計算機視覺 |
| Course description | This course will cover fundamental knowledge and advanced topics in image processing and computer vision, including feature detection, segmentation, motion estimation, panorama construction, 3D reconstruction, scene detection and classification, color image processing and restoration. Applications in computer graphics will also be introduced, including image transformation, and camera calibration. Basic concepts of related algorithms and mathematic background will be discussed. 本科將會介紹圖像處理和計算機視覺基礎知識和進階主題, 包括特徵檢測，圖像分割，運動估計，全景圖構建，三維重搆，場景識別和分類，彩色圖像處理和恢復。本科也將會概觀介紹計算機視覺技術在圖形學的應用，包括圖像變換相機標定。本科會討論相關算法的基本概念和數學背景。 |
| Unit(s) | 3 |
| Course level | Postgraduate |
| Exclusion | CMSC5711 or CSCI5280 |
| Semester | 1 or 2 |
| Grading basis | Graded |
| Grade Descriptors | A/A-: EXCELLENT – exceptionally good performance and far exceeding expectation in all or most of the course learning outcomes; demonstration of superior understanding of the subject matter, the ability to analyze problems and apply extensive knowledge, and skillful use of concepts and materials to derive proper solutions. B+/B/B-: GOOD – good performance in all course learning outcomes and exceeding expectation in some of them; demonstration of good understanding of the subject matter and the ability to use proper concepts and materials to solve most of the problems encountered. C+/C/C-: FAIR – adequate performance and meeting expectation in all course learning outcomes; demonstration of adequate understanding of the subject matter and the ability to solve simple problems. D+/D: MARGINAL – performance barely meets the expectation in the essential course learning outcomes; demonstration of partial understanding of the subject matter and the ability to solve simple problems. F: FAILURE – performance does not meet the expectation in the essential course learning outcomes; demonstration of serious deficiencies and the need to retake the course. |
| Learning outcomes | At the end of the course of studies, students will have acquired the ability to 1. Understand basic knowledge and algorithms in computer vision. 2. Use Matlab in computer vision programming. 3. Perform image transformation in the color and spatial domains. |
| Assessment (for reference only) |
Essay test or exam: 25% Others: 75% |
| Recommended Reading List | 1. Computer Vision: A Modern Approach, 2nd Edition, Forsyth & Ponce, Pearson, 2011. 2. Digital Image Processing, 3rd Edition, Gonzalez and Woods, Prentice Hall, 2008. 3. Multiple View Geometry in Computer Vision, 2nd Edition, Richard Hartley and Andrew Zisserman, Cambridge University Press, March 2004. |
| CSCIN programme learning outcomes | Course mapping |
| Upon completion of their studies, students will be able to: | |
| 1. identify, formulate, and solve computer science problems (K/S); | TP |
| 2. design, implement, test, and evaluate a computer system, component, or algorithm to meet desired needs (K/S); |
T |
| 3. receive the broad education necessary to understand the impact of computer science solutions in a global and societal context (K/V); | |
| 4. communicate effectively (S/V); |
|
| 5. succeed in research or industry related to computer science (K/S/V); |
T |
| 6. have solid knowledge in computer science and engineering, including programming and languages, algorithms, theory, databases, etc. (K/S); | TP |
| 7. integrate well into and contribute to the local society and the global community related to computer science (K/S/V); | |
| 8. practise high standard of professional ethics (V); | |
| 9. draw on and integrate knowledge from many related areas (K/S/V); |
T |
| Remarks: K = Knowledge outcomes; S = Skills outcomes; V = Values and attitude outcomes; T = Teach; P = Practice; M = Measured | |