A stereo vision based craft shock correlation on rough sea
PhDs and postgraduate research
Self-funded PhD students only
School of Mechanical and Design Engineering
October and February
Applications accepted all year round
The work on this project will:
- Implement existing stereo-vision pipelines of epipolar search approach, and variational surface shape model based dense 3D reconstruction of moving wave
- Develop physics wave model to simulate in detail wave-induced load on the vessel
- Modify the variational approach to a sparse surface wave model by incorporating a statistical model of hydrodynamics in the optimisation functionals for matching to allow real time estimation of wave propagation parameters
The project intents to develop a stereo-vision based feature extraction pipeline of oncoming waves at close-proximity. The machine vision system will be deployed on fast marine crafts as a key part of a shock mitigation controller for autonomous surface vehicle that considers the well-being of its human occupants.
The outcome will devise understanding and correlation between close-range wave characteristics with human biomechanic responses to mechanical shock and vibration at sea. The results will be equally relevant to autonomous off-road vehicles with human occupants.
The School of Mechanical and Design Engineering owns two unmanned surface test vehicles and a FLIR® stereo vision camera system dedicated to this project for wave data collection and machine vision algorithm validation. The project will start with comparing and adapting existing stereo vision techniques to achieve off-line wave characterisation. The new in-the-loop feature detector and the unique close-wave imagery data made available will make a significant contribution to the wider machine vision community for outdoor mobile systems and the marine industry.
The visual features will form an important part of the shock-mitigating navigational decisions that are key to the future surface vehicles for safer transport of personnel and casualties. The project is aligned with the University’s vision to build global and national partnership through the boundary-breaking themes of future transportation and intelligent systems.
The successful candidate is expected to collaborate with project partners from areas of visual computing, hydrodynamic modelling, biomechanics and industrial partners in the maritime industry. Attendance at conferences, project meetings, and workshops is also anticipated.
Fees and funding
Funding availability: Self-funded PhD students only.
PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).
2020/2021 fees (applicable for October 2020 and February 2021 start)
Home/EU/CI full-time students: £4,407 p/a*
Home/EU/CI part-time students: £2,204 p/a*
International full-time students: £16,400 p/a*
International part-time students: £8,200 p/a*
*All fees are subject to annual increase
You'll need an upper second class honours degree from an internationally recognised university or a Master’s degree in Robotics, Computer Science, Electronics, Mechanics, Mathematics, Physics or a similar discipline. In exceptional cases, we may consider equivalent professional experience and/or qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.
How to apply
We’d encourage you to contact Dr Ya Huang at firstname.lastname@example.org to discuss your interest before you apply, quoting the project code.
Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. An extended statement as to how you might address the proposal would be welcomed.
Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this self-funded PhD opportunity you must quote project code SMDE4610220 when applying.