This is an archived syllabus from 2013-2014
COMP61342 Computer Vision syllabus 2013-2014
COMP61342 Computer Vision
Level 6
Credits: 15
Enrolled students: 15
Course leader: Aphrodite Galata
Additional staff: view all staff
Assessment methods
- 50% Written exam
- 50% Coursework
| Semester | Event | Location | Day | Time | Group |
|---|---|---|---|---|---|
| Sem 2 P4 | Lecture | 2.15 | Thu | 09:00 - 09:00 | - |
- Making Sense of Complex Data
- Computer Science units for ACSwITM students (semester 2)
Overview
This unit will give students a foundation in the subject of machine vision. This will involve gaining familiarity with algorithms for low-level and intermediate-level processing and considering the organisation of practical systems. Particular emphasis will be placed on the importance of representation in making explicit prior knowledge, control strategy and interpretting hypotheses.
This course unit treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic and statistical approaches. As such, it will also give students a foundation in the statistical methods of image analysis.
Topics covered in the course include perception of 3D scene structure from stereo; image filtering, smoothing, edge detection; segmentation and grouping; learning, recognition, and search; tracking and motion estimation; behaviour modelling.
Emphasis will also be placed on the importance of understanding algorithmic stability and optimality as a framework for algorithmic design and research methodology.
This course unit is designed for students that are interested in Computer Vision, Artificial Intelligence, or Machine Learning. This course unit is also appropriate for students with an interest in Computer Graphics.
Aims
To introduce the basic concepts and algorithmic tools of computer vision.
To introduce the problems of building practical vision systems.
To explore the role of representation and inference.
To explore the statistical processes of image understanding and develop an understanding of advanced concepts and algorithms.
To discuss novel approaches to designing vision systems that learn.
To develop skills in evaluation of algorithms for the purposes of understanding research publications in this area.
Teaching methods
Lectures
1 day per week (5 weeks)
Feedback methods
The assessment for this course unit is based on a combination of coursework and a closed-book exam. The coursework consists of: reports on a set of practical assignments carried out using MATLAB, an essay based on reading a collection of journal papers and a group presentation on selected research papers. Feedback will be provided via moodle and in person after the group presentations.Study hours
Employability skills
- Analytical skills
- Group/team working
- Oral communication
- Research
- Written communication
Learning outcomes
On successful completion of this unit, a student will be able to:
Learning outcomes are detailed on the COMP61342 course unit syllabus page on the School of Computer Science's website for current students.
Reading list
No reading list found for COMP61342.
Additional notes
Course unit materials
Links to course unit teaching materials can be found on the School of Computer Science website for current students.