Overview

MSc Computer Graphics, Vision & Imaging provides training in computer graphics, virtual reality, machine vision and imaging technology from world-leading experts. Research activities include geometric acquisition and 3D fabrication; real-time photo-realistic rendering; mixed and augmented reality; face recognition; content-based image-database search; video-texture modelling; depth perception in stereo vision; colour imaging for industrial inspection; and tracking for SLAM (simultaneous localisation and mapping).

The common theme across the programme is to understand how to make virtual reality more effective; we carry out experiments with participants to examine just what makes a difference to their sense of presence in the virtual environment. Real world scenarios like these train our students to bridge the gap between engineering, technology, creativity and design.

Graduates will understand the mathematical principles underlying the development and application of new techniques in computer graphics and computer vision and will be aware of the range of algorithms and approaches available, and be able to design, develop and evaluate appropriate algorithms and methods for new problems, emerging technologies, and applications.

Core Modules

Mathematical Methods Algorithms & Implementations

Mathematical Methods Algorithms & Implementations

To provide a rigorous mathematical approach: in particular to define standard notations for consistent usage in other modules. To present relevant theories and results. To develop algorithmic approach from mathematical formulation through to hardware implications.

Further syllabus information can be found here.

Image Processing

Image Processing

The first half of this module introduces the digital image, describes the main characteristics of monochrome digital images, how they are represented and how they differ from graphics objects. It covers basic algorithms for image manipulation, characterisation, segmentation and feature extraction in direct space. The second half of the module proceeds to a more formal treatment of image filtering with some indication of the role and implications of Fourier space, and more advanced characterisation and feature detection techniques such as edge and corner detection, together with multiresolution methods, treatment of colour images and template matching techniques.

Further syllabus information can be found here.

Computer Graphics

Computer Graphics

This module aims to introduce the fundamental concepts of 3D computer graphics and give the students all the knowledge needed for creating an image of a virtual world from first principles. Students will be able to define a virtual world and create images of it. They will know how to write a basic ray tracer, and use a graphics library such as OpenGl (or equivalent).

Further syllabus information can be found here.

Research Methods & Reading

Research Methods & Reading

The aim of this module is to introduce students to research methods and guide them through writing a critical literature review of their chosen area.

Further syllabus information can be found here.

Optional Modules

Machine Vision

Machine Vision

This module addresses algorithms for automated computer vision. It focuses on building mathematical models of images and objects and using these to perform inference. Students will learn how to use these models to automatically find, segment and track objects in scenes, perform face recognition and build three-dimensional models from images.

Further syllabus information can be found here.

Graphical Models

Graphical Models

 

This module provides an entry into probabilistic modeling and reasoning, primarily of discrete variable systems. Very little continuous variable calculus is required, and students more familiar with discrete mathematics should find the course digestible. The emphasis is to demonstrate the potential applications of the techniques in plausible real-world scenarios related to information retrieval and analysis. Concrete challenges include questionnaire analysis, low-density parity check error correction, and collaborative filtering of Netflix data.

Further syllabus information can be found here.

Virtual Environments

Virtual Environments

The aim of this module is to introduce students to the main concepts and practical issues in constructing and understanding Virtual Environments, and how people respond to a VE experience. Given the background of the course teachers, the focus on the technical side will be more on the visual aspects of VEs. A central theme of the course will also be that the understanding of VEs can be best understood through the concepts of presence and shared presence.

Further syllabus information can be found here.

Geometry of Images

Geometry of Images

The aim of this module is to introduce the generalisation of image processing to n-Dimensional data: volume data, scale space, time-series and vectorial data. Students will understand the principles of image processing in n-dimensions, time-series analysis and scale space, and to understand the relations between geometric objects and sampled images.

Further syllabus information can be found here.

Acquisition & Processing of 3D Geometry

Acquisition & Processing of 3D Geometry

This  module will expose students to the challenges and potential of geometry processing in relevant application areas. It aims to explain how to use acquire 3D model, and subsequently process, analyze, and manipulate the data, and familiarize students with handling real data sets. Students will gain necessary practical skills to work directly with real-world 3D data, and be able to formulate and solve problems using the geometric tools they learn as part of the module.

Further syllabus information can be found here.

Inverse Problems in Imaging

Inverse Problems in Imaging

The aim of this module is to introduce the concepts of optimisation, and appropriate mathematical and numerical tools applications in image processing and image reconstruction. Students will understand the principles of optimisation and acquire skills in mathematical methods and programming techniques.

Further syllabus information can be found here.

Computational Modelling for Biomedical Imaging

Computational Modelling for Biomedical Imaging

The aim of this module is to expose students to the challenges and potential of computational modelling in a key application area. To explain how to use models to learn about the world. To teach parameter estimation techniques through practical examples. To familiarize students with handling real data sets.

Further syllabus information can be found here.

Computational Photography & Capture

Computational Photography & Capture

The module is designed to be self-contained, introducing the theoretical and practical aspects of modern photography and capture algorithms to students with only limited mathematical background. The two primary aims are i) to introduce universal models of colour, computer-controlled cameras, lighting and shape capture, and ii) to motivate students to choose among the topics presented for either continuing study (for those considering MSc’s and PhD’s) or future careers in the fields of advanced imaging

Further syllabus information can be found here.

Information Processing in Medical Imaging

Information Processing in Medical Imaging

The essence of medical image computing is to derive information from medical images for clinical diagnosis, therapy or to improve our understanding of function and disease. This module focusses on algorithms and software for obtaining this information. The module is provided by members of the Centre for Medical Image Computing.

Further syllabus information can be found here.

Entry Requirements

A minimum of an upper-second class UK Bachelor's degree in computer science, electrical engineering or mathematics, or an overseas qualification of an equivalent standard. Relevant work experience may also be taken into account. 

English Language Requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency. The English language level for this programme is: Good. Further information can be found on our English language requirements page.

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

Fees & Funding

UK/EU fees:     £11,090 (FT)
                       £5,725 (PT)

Overseas fees: £24,400 (FT)
                      £12,310 (PT)

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the