About the Project
In this project, building on research in the computer vision community, we will develop image processing methods to help us to ‘see into shadows’ and thereby to better classify the coastal features found in images. Our overarching aim is to develop a vision system which - by seeing into shadows - will significantly improve the accuracy of surveys of the coastal environment.
This exciting project will see you working with a team of computing and natural scientists at the cutting edge of remote sensing research. You will characterise the capture device (measuring the camera’s spectral response ) and the statistics of the physical world that is being surveyed, to understand the spectral properties of the coastal environment. You will undertake modelling of the coastal environment in collaboration with Cefas, using a large annotated imagery dataset, with an emphasis on identifying the same image features seen in and out of shadows. You will repurpose  and  for algorithm development, incorporating our understanding of the camera and the physical world. A particularly novel aspect is that we will incorporate near-infrared into the algorithm formulation. There is evidence  that near-infrared can help distinguish shadowed and non-shadowed areas.
The candidate - who should have a scientific or engineering background - will be involved in all aspects of the project. They will be trained in measurement and calibration at the world-leading UEA Colour Lab and will work directly with Cefas. The developed algorithms will be prototyped in Cefas’ classification framework.
This project has been shortlisted for funding by the ARIES NERC DTP.
Successful candidates who meet UKRI’s eligibility criteria are awarded a NERC studentship covering fees, stipend (£15,285 p.a., 2020-21) and research funding. International applicants (EU/non-EU) are eligible for fully-funded studentships. Please note ARIES funding does not cover visa costs (including immigration health surcharge) or other additional costs associated with relocation to the UK.
Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses.
ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside significant relevant non-academic experience.
For further information, please visit http://www.aries-dtp.ac.uk
For more information on the supervisor for this project, please go here https://people.uea.ac.uk/g_finlayson
The type of programme is a PHD
Start of the project is 1st October 2021
The mode of study is full or part time (visa restrictions may apply)
Studentship length is 3.5 years
Acceptable first degree in Science/Engineering.
2. G.D. Finlayson, “Rank-Based Spectral Sensitivity Estimation,” J. Optical Society of America A, 2016
3. G.D. Finlayson et al., “Colour Constancy at pixel,” J. Optical Society of America A, 2001
4. W.Madden et al., “Illumination Invariant Imaging: Applications in Robust Vision-based Localisation, Mapping and Classification for Autonomous Vehicles’’, Int. Conf. On Robotics and Automation, 2014.
5. D. Rüfenach et al., “Automatic and Accurate Shadow Detection using Near-Infrared Information,” IEEE Trans. PAMI, 2014.
Why not add a message here
Based on your current searches we recommend the following search filters.
Based on your current search criteria we thought you might be interested in these.