PhD Studentship - Learning to Recognise Dynamic Visual Content from Broadcast Footage

PhD Studentship - Learning to Recognise Dynamic Visual Content from Broadcast Footage

Posted by Rebecca Martin on Mon, 11/07/2011 - 12:14

Supervision: Dr Mark Everingham, University of Leeds

Deadline: Open until filled

Project Description:

A PhD studentship is available as part of an EPSRC funded project on "Learning to Recognise Dynamic Visual Content from Broadcast Footage" being jointly undertaken by the University of Leeds (Mark Everingham), the University of Oxford (Andrew Zisserman), and the University of Surrey (Richard Bowden).

The objective of the project is to develop automated tools that allow temporal visual content, such as a human gesturing, using sign language, or interacting with objects or other humans, to be learnt from standard TV broadcast signals using the transmitted annotation in the form of subtitles and annotation for the visually impaired as supervision. This requires the development of models of the visual appearance and dynamics of actions, and learning methods which can train such models using the weak supervision provided by the subtitles. Once the models have been learnt they can then be used without supervision, e.g. for sign language interpretation or automatic description of the content of video footage, and during the project demonstrators will be engineered for both of these applications.

The student will focus on the development of visual descriptors and learning algorithms for sign language and action recognition in broadcast video. S/he will be based in the School of Computing at the University of Leeds, and will be supervised by Dr Mark Everingham (http://www.comp.leeds.ac.uk/me/).

Funding Notes: The studentship is funded by an EPSRC project studentship and will start from 1st October 2011 or as soon as possible thereafter. The studentship is funded for 3 years and covers Home/EU fees and maintenance at the standard EPSRC rate (currently £13,590 per annum). Applications are welcome from overseas students, but such students would have to provide the difference between the UK/EU and the overseas student rates for university fees from some other source, such as a scholarship or personal funds.

Academic Staff Contact Details: Dr Mark Everingham. M.Everingham(at)leeds.ac.uk (for informal enquiries about the project only - do not send applications to this address).

Entry Requirements: The PhD candidate should have or expect to obtain a first class or strong 2.1 honours degree in computer science, mathematics, or related discipline. The following qualities are desirable: demonstrable experience in computer vision or machine learning; excellent record of academic and/or professional achievement; strong mathematical skills; strong programming skills, especially C/C++ and Matlab; good written and spoken communication skills in English.

Details on how to apply can be found at:

http://www.leeds.ac.uk/rds/prospective_students/apply/I_want_to_apply.html

Application Procedure: Formal applications for research degree study must be made either on line through the University website, or on the University's application form. Detailed information of how to apply on line can be found at: http://www.leeds.ac.uk/students/apply_research.htm

The paper application form is available at: http://www.leeds.ac.uk/rsa/prospective_students/apply/I_want_to_apply.html

Please return the completed application form to the Research Degrees & Scholarships Office, University of Leeds, LS2 9JT.

Please note, if you intend to send academic references we can only accept them if they are on official letter headed paper and contain an original signature and stamp; they must arrive in sealed envelopes. Alternatively, the School will contact your named academic referees directly.