Computer Vision
| Code | School | Level | Credits | Semesters |
| COMP3007 | Computer Science | 3 | 20 | Spring UK |
- Code
- COMP3007
- School
- Computer Science
- Level
- 3
- 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.
Target Students
Available to Level 3 students in the School of Computer 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 COMP4106. 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 11 weeks
- One 2-hour lecture each week for 11 weeks
- One 2-hour computing each week for 11 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.
Assessment
- 40% Coursework: MATLAB project and 4-page report. The reassessment for this module will be 100% examination.
- 60% Exam 1 (2-hour): 2- hr examination. The reassessment for this module will be 100% Examination.
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.
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