Geometric superpixel clustering and segmentation

Description

About the Project:

Are you familiar with machine learning? Do you have a solid mathematical background and a keen interest in image processing? Are you interested in pushing the boundaries of mathematical and computational research?

This research project in applied mathematics aims to develop and analyse novel clustering and image segmentation approaches to extract geometric features from large and possibly noisy images. A prime application is object contouring where the aim is to automatically extract object boundaries (e.g., in handwritten documents, drawings, satellite maps, microscopy images) that can be analysed or further manipulated (scaled, rotated, simplified). Current approaches perform the extraction on the pixel level, but this comes at a high computational cost and sensitivity to noise. Noise is a substantial problem in all imaging systems, particularly in high-speed imaging systems.

However, exciting recent advances in constrained clustering are opening up a different route to tackle this problem: pixels can be clustered into so-called superpixels with quadratic boundaries and noise-suppressing properties. In this innovative project you will deploy your analytical and computational skills to develop, analyse, and test such geometric superpixel approaches to cluster the pixels and extract geometric features. The project will begin by exploring various models to utilize the superpixel boundary description to perform object segmentations (year one), progress through considering different noise and image resolution models (year two), culminating in implementations, tests, and evaluations involving simulated and real data (year three).

As a member of the Centre for Mathematical Imaging Techniques (CMIT) you will be part of an innovative, inspiring, and dynamic team that develops mathematical imaging techniques at the research frontiers of various application disciplines. This PhD project has good opportunities to collaborate with international CMIT partners, in particular, from the DTU (Denmark), TUM (Germany), and NTHU (Taiwan).

 

Start Date: 1st October 2023

Further Details:

This PhD project is funded by The Faculty of Science & Engineering at The University of Liverpool and will start on 1st October 2023.

Successful candidates who meet the University of Liverpool eligibility criteria will be awarded a Faculty of Science & Engineering studentship for 3.5 years, covering UK tuition fees and an annual tax-free stipend (e.g. £17,688 p.a. for 2022-23).

Faculty of Science & Engineering students benefit from bespoke graduate training and £5,000 for training, travel and conferences.

The Faculty of Science & Engineering is committed to equality, diversity, widening participation and inclusion. Academic qualifications are considered alongside non-academic experience. Our recruitment process considers potential with the same weighting as past experience. Students must complete a personal statement profoma and ensure this is included in their online application.

How to Apply:

All applicants must complete the personal statement proforma. This is instead of a normal personal/supporting statement/cover letter. The proforma is designed to standardise this part of the application to minimise the difference between those who are given support and those who are not. The proforma can be found here: https://tinyurl.com/ym2ycne4. More information on the application process can be found here: https://tinyurl.com/mwn5952t. When applying online, students should ensure they include the department name in the ‘Programme Applied For’ section of the online form, as well as the Faculty of Science & Engineering as the ‘studentship type’ in the finance section.

https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ 

Availability

Open to UK applicants

Funding information

Funded studentship

UK students are only eligible for a fully-funded  Faculty of Science & Engineering studentship; overseas students are eligible to apply if they can financially cover the difference in UK and Overseas tuition fees, cover the costs of their student visa, NHS health surcharge, travel insurance and transport to the UK, as these are excluded from the funding.

Supervisors