The Sullivan Doctoral Thesis Prize


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.

Acceptance Criteria

We welcome submissions from anyone who has submitted their PhD thesis at a university in the United Kingdom within the 2024 academic year. Any topic that is applicable under the current BMVC call for papers is valid.


Candidates should submit:

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:

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:

Submission deadline: 13 September 2024

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 Award Winners Photos

Thesis Prive Awarding Thesis Prive Awarding

Thesis Prive Awarding Thesis Prive Awarding

Previous winners

Year Winner Institution Thesis
2022Shangzhe WuUniversity of OxfordUnsupervised Learning of 3D Objects in the Wild
2021Jaime Spencer MartinUniversity of SurreyLearning Generic Deep Feature Representations
2020Dimitrios KolliasImperial College LondonAffect Recognition & Generation in-the-wild
2019Alex KendallUniversity of CambridgeGeometry and Uncertainty in Deep Learning for Computer Vision
2018Oscar MendezUniversity of SurreyCollaborative Strategies for Autonomous Localisation, 3D Reconstruction and Pathplanning
2017Karel LebedaUniversity of Surrey2D and 3D Tracking and Modelling
2016Xiatian ZhuQueen Mary, University of LondonSemantic Structure Discovery in Surveillance Videos
2015Vibhav VineetOxford Brookes UniversityRecognition, Reorganisation, Reconstruction and Reinteraction for Scene Understanding
2014Mattias HeinrichUniversity of OxfordDeformable lung registration for pulmonary image analysis of MRI and CT scans
2013Patrick Ott
(joint winner)
University of LeedsSegmentation Features, Visibility Modeling and Shared Parts for Object Detection
2013Shoaib Ehsan
(joint winner)
University of EssexImproving the Effectiveness of Local Feature Detection
2012Marco PaladiniQueen Mary, University of LondonDeformable and Articulated 3D Reconstruction from monocular video sequences
2011Charles BibbyUniversity of OxfordProbabilistic Methods for Enhanced Marine Situational Awareness
2010Olly OechsleUniversity of EssexTowards the Automatic Construction of Machine Vision Systems using Genetic Programming
2009Pawan Kumar MudigondaOxford Brookes UniversityCombinatorial and Convex Optimization for Probabilistic Models in Computer Vision
2008Pushmeet KohliOxford Brookes UniversityMinimizing Dynamic and Higher Order Energy Functions using Graph Cuts
2007Josef SivicUniversity of OxfordEfficient visual search of images and videos
2006Rob FergusUniversity of OxfordVisual Object Category Recognition
2005Björn StengerUniversity of CambridgeModel-based hand tracking using a hierarchical Bayesian filter
2004Jonathon StarckUniversity of SurreyHuman Modelling from Multiple Views
2003Rhodri DaviesUniversity of ManchesterLearning Shape: Optimal Models for Analysing Shape Variability
2002Albert ChungUniversity of OxfordVessel and aneurysm reconstruction using speed and flow coherence information in phase contrast magnetic-resonance angiograms.
2001Gareth J EdwardsUniversity of ManchesterLearning to identify faces in images and sequences.
2000Richard BowdenBrunel UniversityLearning non-linear Models of Shape and Motion
1999Neil JohnsonUniversity of LeedsLearning Object Behaviour Models