Computer Vision
| Code | School | Level | Credits | Semesters |
| COMP3065 | School of Computer Science | 3 | 20 | Spring China |
- Code
- COMP3065
- School
- School of Computer Science
- Level
- 3
- Credits
- 20
- Semesters
- Spring China
Summary
You’ll examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You’ll cover a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, recovery of three-dimensional shape and analysis of motion. You’ll learn how to implement some of these methods in the industry-standard programming environment MATLAB. You’ll spend around four hours a week in lectures, tutorial and laboratory sessions.
Target Students
Part II undergraduate students in the School of Computer Science. Also available to students from other Schools with the agreement of the module convenor. This module is part of the AI, Modelling and Optimisation theme in the School of Computer Science. Available to JYA/Erasmus students.
Classes
- One 1-hour tutorial each week for 12 weeks
- One 2-hour lecture each week for 12 weeks
- One 1-hour computing each week for 12 weeks
Method and Frequency of Class and timing of class: one two-hour lectures per week, with practical problems being tackled in a single coursework. One one-hour lab session will be held, in which students work on practical programming assignments and their coursework. And one- hour tutorial session will be held discussing further lecture materials and lab solutions.
Assessment
- 40% coursework 1: Practical programming project in Python including project proposal, codes and final report
- 60% Exam 1 (2-hour): Two-hour written exam
Educational Aims
To provide a grounding in existing techniques and current research in computer vision.To give experience in implementing computer vision solutions to real world problems.Learning Outcomes
Understanding of current techniques in image analysis and computer vision and an awareness of their limitations. An appreciation of the underlying mathematical principles of computer vision. Experience in designing and implementing computer vision systems. Intellectual Skills: Apply knowledge of computer vision techniques to particular tasks. Evalutate and compare competing approaches to vision tasks. Evaluate vision systems. Professional Skills: Develop a working knowledge of computer vision/image analysis algorithms and evaluate the applicability of various algorithms and operations to particular tasks. Transferable Skills: Apply knowledge of the methods and approaches presented to problem domains use the available resources (libraries, internet, etc) to supplement the course material.
Conveners
- Dr Tianxiang Cui
- Dr Zheng Lu