Computer Vision
| Code | School | Level | Credits | Semesters |
| COMP3029 | Computer Science | 3 | 20 | Spring Malaysia |
- Code
- COMP3029
- School
- Computer Science
- Level
- 3
- Credits
- 20
- Semesters
- Spring Malaysia
Summary
You'll examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You'll learn a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, image segmentation, recovery of three-dimensional shape and analysis of motion. These problems will be approached with both traditional and modern Computer Vision approaches including Deep Learning. You will spend 4 hours per week in lectures, tutorials, and computer classes for this module. The module involves some mathematical understanding, and requires a good programming ability.
Target Students
Available to Level 3 students in the School of Computer Science. Available to inter-campus mobility students and other exchange students in computer science. This module is not available to students not listed above, without explicit approval from the module convenor(s). This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
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
Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester Further Activity Details: Activities may take place every teaching week of the Semester or only in specified weeks. It is usually specified above if an activity only takes place in some weeks of a Semester.
Assessment
- 40% Coursework 1: Coding project and report (in style of a scientific paper). Reassessment is 100% exam.
- 60% Exam 1 (2-hour): 2 hr examination. Reassessment is 100% exam.
Educational Aims
1. To provide a grounding in existing techniques and current research in computer vision.2. To give experience in implementing computer vision solutions to real world problems.Learning Outcomes
Knowledge and Understanding:
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 Iman Liao Yi