Self-Learning for Computer Vision Asset Management
PhDs and postgraduate research
Self-funded PhD students only
School of Computing
Applications accepted all year round
The work on this project could involve:
- Adaptive and self-learning Neural Network structures
- Faster R-CNN, RetinaNet, YOLO.
- Multi-Sensor input for self-learning
Artificial neural networks are widely used to undertake complex image analysis, and carry out tasks such as identifying objects in a noisy environment. Typically Neural Networks will be trained in advance. Performance of the system is hence dependent on the quality and amount of training.
Artificial neural networks require vast quantities of accurately labelled training data, e.g. thousands of images with objects accurately defined. This is a time consuming and costly
step. In many situations environmental influences such as perspective, reflections and ambient lighting will affect how the system performs.
This project will investigate ways of allowing Neural Network Systems to apply self-learning post deployment and hence continue to improve performance past the initial training stage. Input from multiple sensors will be explored and how this sensor collaboration can improve the self-learning aspect for asset management applications with multi-sensor coverage.
The work will be undertaken in the University of Portsmouth, as part of a multi-disciplinary team of Engineers and Computer Scientists keen to explore new avenues for the use of
Artificial Neural Networks in everyday life.
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).
You'll need a good first degree from an internationally recognised university or a Master’s degree in an appropriate subject. 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 I.kagalidis (firstname.lastname@example.org) to discuss your interest before you apply, quoting the project code.
When you are ready to apply, you can use our online application form. 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. Our ‘How to Apply’ page offers further guidance on the PhD application process.
If you want to be considered for this PhD opportunity you must quote project code COMP5020220 when applying.