PhD in Computer Science: Development of new, automated methods in image processing for photogrammetry RTI (Reflectance Transformation Imaging). Ref: 2177

About the award

Primary supervisor: Dr Jacqueline Christmas

Secondary supervisor: Dr Judith Bannerman

Location: Streatham campus, Exeter

Project description:

RTI is an image processing technique that combines multiple digital photographs of an object, lit from different directions, into a representation that allows the user to explore the shape and colour of its surface interactively.  Mathematical techniques are used to enhance key features and to build a three-dimensional representation of the object that captures surface detail not visible to the naked eye.

This project aims to enhance and expand existing photogrammetry RTI methods by developing and implementing new, automated image processing techniques, with a particular emphasis on machine learning and artificial intelligence approaches.

The immediate application is to desk-top scale cultural heritage items, but it is anticipated that the project will broaden from desk-top imaging to imaging both in the field and through microscopes.  The ultimate aim is to deliver a low-cost system, based on off-the-shelf components that can be used by non-technical operators.

You will join a growing machine learning group at Exeter, working directly with Dr Jacqueline Christmas and Dr Judith Bannerman, and we will be collaborating with Prof George Bevan of Queen’s University, Canada, who is an expert in the use of these computational photographic techniques and has a particular interest in enhancing inscriptions on weathered surfaces.

Please be advised that a significant part of the role will involve the use of flash photography.

This post is to start on 9th January 2017.

For informal enquiries about the project, please contact Dr Jacqueline Christmas, J.T.Christmas@exeter.ac.uk.

Academic entry requirements:

Applicants should have or expect to achieve at least a 2:1 Honours degree, or equivalent, in Computer Science or a closely related subject.  They should have strong programming skills, an aptitude for mathematics, and an enthusiasm for research into image processing and machine learning.

Residency entry requirements:

This studentship is available to UK and EU students. International students can apply and pay the difference in tuition fees.

Summary

Application deadline:23rd October 2016
Number of awards:1
Value:3.5 year studentship including UK/EU tuition fees plus a stipend equivalent to the RCUK rate (£14,296 for 2016/17)
Duration of award:per year
Contact: CEMPS PGR Admissionsemps-pgr-ad@exeter.ac.uk

How to apply

To apply, complete the online form. You will be asked to submit some personal details and upload a full CV, covering letter and details of two academic referees. Your covering letter should outline your academic interests, prior research experience and reasons for wishing to undertake this project.

You may also be asked to upload verified transcripts of your most academic qualification.

Interviews will be held the in the first two weeks in November 2016.

Please quote reference 2177 on your application and in any correspondence about this studentship.

For general enquiries please contact the PGR Admissions Administrator at emps-pgr-ad@exeter.ac.uk.