About Us
Find out more about our team and facilities.
Welcome to the Vision, Learning and Control (VLC) research group. We are a highly numerate team covering much of the central theory in Electronics, Electrical Engineering and Computer Science. We cover a rich spread of technological areas encompassing control, machine learning and computer vision with a sizeable number of academic staff, PhD researchers and postdocs.
Research
Research in VLC encompasses all aspects of Computer Vision, Machine Learning and Control.
Find Us
Southampton 'space archaeology' solution...
Postgraduate researcher Iris Kramer from the University of Southampton is being supported by the Royal Academy of Engineering as she scales deep learning software that identifies buried ancient sites from space.
Read MoreAI-powered archaeology and team sports insight...
Innovative technologies developed by computer scientists at the University of Southampton will be unveiled in an online edition of the world’s largest and most influential technology show, CES 2021.
Read MoreAI archaeologist tops triple investment success...
Archaeologist turned computer scientist Iris Kramer secured a record-breaking ã770,000 valuation for her deep learning tool for archaeological surveys in a Dragons Den-style event at the University of Southampton.
Read MoreRecent VLC publications
Modelling digital and manual contact tracing for COVID-19.: Are low uptakes and missed contacts deal-breakers? - Andrei Rusu, Rémi Emonet and Katayoun Farrahi
Type: Article
| 2021
Type: Article
| 2021
Facial profiles recognition using comparative facial soft biometrics - Malak Alamri and Sasan Mahmoodi
Type: Conference or Workshop Item
| 2020
On quantifying the role of exogenous macro-economic information in machine learning for modelling financial data - Luis Jairo Montesdeoca Bermudez
Type: Thesis
| 2020
| University of Southampton
COPD detection using three-dimensional Gaussian Markov random fields based on binary features - Yasseen, Hamad Al Makady, Sasan Mahmoodi and Michael Bennett
Type: Conference or Workshop Item
| 2020
| IEEE