The British Machine Vision Association : The Sullivan Doctoral Thesis Prize

The Sullivan Doctoral Thesis Prize

Mission for 2023

Encourage wider participation, streamline submission and review process, widen scope for topics of interest, and expand marking criteria to recognise a more diverse range of talent.

Submission details are provided below. Please note that submissions are now via the new CMT website.

Overview

The Sullivan Thesis Prize celebrates early stage researchers working in the field of computer vision and related areas in the United Kingdom. The prize will be presented at the annual British Machine Vision Conference (BMVC)conference dinner. The prize fund is provided by the British Machine Vision Association (BMVA) to commemorate the contribution made by the late Professor Geoff Sullivan.

While normally awarded annually, in 2023 we have widened the eligibility criteria due to the impact of COVID-19. We want all applicable candidates to feel empowered to apply, so please feel free to contact the award organizers if you have any further questions, or are unsure about eligibility.

Acceptance Criteria

We welcome submissions from anyone who has submitted their PhD thesis at a university in the United Kingdom between 1st April 2019 and 31st August 2023. Any topic that is applicable under the current BMVC call for papers is valid.

Submission

Candidates should submit a short summary (max 500 words) providing an overview of the contributions made during their PhD, and a digital copy of their completed PhD Thesis. If available, it is recommended that the external examiner(s) report for the Thesis is also included, which can either be post-viva or post-corrections (preferred).

[Optional] Candidates may also provide additional supporting evidence, which may include but is not limited to:

Up to 3 accepted paper(s) as first author (or equal contributor)
Blog post(s)
Conference poster(s)
Details of External talks (e.g. conferences)
Profile page(s) on Google Scholar, ResearchGate etc.
Report from an industrial placement
Endorsement from either an academic or industrial supervisor
Demos

Please submit evidence in PDF format, or provide an accessible link. If any of the evidence is confidential please get permission before submitting.

Submissions should be made using CMT: https://cmt3.research.microsoft.com/SullivanThesisPrize2023.

Submission deadline: 15 September 2023

Award process

After the submission deadline, each application will be assessed by a panel of experts from academia and industry. Applications will be marked across a broad range of criteria including originality, academic/industrial impact, breadth and depth.

Any Questions?

Please email Sullivan Thesis Prize Chairs at Dr Alex Mackin.

Previous winners

Year	Winner	Institution	Thesis
2017	Karel Lebeda	University of Surrey	2D and 3D Tracking and Modelling
2016	Xiatian Zhu	Queen Mary, University of London	Semantic Structure Discovery in Surveillance Videos
2015	Vibhav Vineet	Oxford Brookes University	Recognition, Reorganisation, Reconstruction and Reinteraction for Scene Understanding
2014	Mattias Heinrich	University of Oxford	Deformable lung registration for pulmonary image analysis of MRI and CT scans
2013	Patrick Ott (joint winner)	University of Leeds	Segmentation Features, Visibility Modeling and Shared Parts for Object Detection
2013	Shoaib Ehsan (joint winner)	University of Essex	Improving the Effectiveness of Local Feature Detection
2012	Marco Paladini	Queen Mary, University of London	Deformable and Articulated 3D Reconstruction from monocular video sequences
2011	Charles Bibby	University of Oxford	Probabilistic Methods for Enhanced Marine Situational Awareness
2010	Olly Oechsle	University of Essex	Towards the Automatic Construction of Machine Vision Systems using Genetic Programming
2009	Pawan Kumar Mudigonda	Oxford Brookes University	Combinatorial and Convex Optimization for Probabilistic Models in Computer Vision
2008	Pushmeet Kohli	Oxford Brookes University	Minimizing Dynamic and Higher Order Energy Functions using Graph Cuts
2007	Josef Sivic	University of Oxford	Efficient visual search of images and videos
2006	Rob Fergus	University of Oxford	Visual Object Category Recognition
2005	Björn Stenger	University of Cambridge	Model-based hand tracking using a hierarchical Bayesian filter
2004	Jonathon Starck	University of Surrey	Human Modelling from Multiple Views
2003	Rhodri Davies	University of Manchester	Learning Shape: Optimal Models for Analysing Shape Variability
2002	Albert Chung	University of Oxford	Vessel and aneurysm reconstruction using speed and flow coherence information in phase contrast magnetic-resonance angiograms.
2001	Gareth J Edwards	University of Manchester	Learning to identify faces in images and sequences.
2000	Richard Bowden	Brunel University	Learning non-linear Models of Shape and Motion
1999	Neil Johnson	University of Leeds	Learning Object Behaviour Models

Regulations

The prize of £750 is considered for presentation annually for the best doctoral thesis.
Submissions must be in the broad areas of computer vision, including computational studies of natural vision.
The submission period is by calendar year.
Valid submission dates are based on the date of the successful viva voce exam, as evidenced by the signed examiners’ reports.
Submissions must be in electronic form. The thesis must first be submitted into the thesis archive. Other submission materials should be emailed to the BMVA Chair.
Accompanying the thesis should be a supporting statement from the research supervisor and a recommendation from the external examiner, including the examiner’s report. These should be in PDF form.
Each submission will be assessed by independent reviewers who will be appointed by the selection panel.

The British Machine Vision Associationand Society for Pattern Recognition