Computer Vision
| Code | School | Level | Credits | Semesters |
| COMP4106 | Computer Science | 4 | 20 | Spring UK |
- Code
- COMP4106
- School
- Computer Science
- Level
- 4
- Credits
- 20
- Semesters
- Spring UK
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, pose estimation, 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 5 hours per week in lectures, tutorials, and computer classes for this module. The module involves some mathematical understanding, and requires a good programming ability. As part of the assessment of this module you will produce a research paper-style report, and deliver a conference-style presentation.
Target Students
Available to Level 4 students in the School of Computer Science. Available for students in MSc Machine Learning in Science. This module is not available to students not listed above without explicit approval from the module convenor(s), and is not available to students taking COMP3007. This module is part of the Artificial Intelligence, Modelling and Optimisation theme in the School of Computer Science.
Assessment
- 30% Coursework 1: Coding project and report (in style of a scientific paper). Reassessment is 100% exam.
- 10% Coursework 2: 15 minutes research-style presentation to a panel of experts, including time for questions. Reassessment is 100% Exam.
- 60% Exam 1 (2-hour): ExamSys exam.
Assessed by end of spring semester
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
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.
• An appreciation of the use of Machine learning methods to computer vision.
Intellectual Skills
• Apply knowledge of computer vision techniques to particular tasks.
• Evaluate 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 different problem domains using the available resources (libraries, internet, etc.).
Conveners
- Dr Valerio Giuffrida
- Dr Andrew French