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
Scientists investigate machine learning sensors...
Researchers from the University of Southampton are using machine learning techniques to develop the next generation of wear sensing in machines such as planes and cars.
Read MoreCentre for Machine Intelligence launched to...
Computer science experts discussed the key challenges surrounding emerging AI and blockchain technologies at the Centre for Machine Intelligence launch
Read MoreYoung university innovators unveil future...
Pioneering research, including an AI-powered video analytics platform, features at the 2018 Consumer Electronics Show in Las Vegas
Read MoreRecent VLC publications
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
| Item availability restricted.
Ridge detection and analysis of susceptibility-weighted magnetic resonance imaging in neonatal hypoxic-ischaemic encephalopathy - Zhen Tang, Sasan Mahmoodi, Srinandan Dasmahapatra, Angela Darker and Brigitte Vollmer
Type: Conference or Workshop Item
| 2020
| Springer
| Item availability restricted.
Type: Thesis
| 2020
| University of Southampton
| Item availability restricted.
A duality-based approach to identification of linear time-varying systems - Joe Alan Roman Flores
Type: Thesis
| 2019
| University of Southampton
| Item availability restricted.
A general framework in single and multi-modality registration for lung imaging analysis using statistical prior shapes - Zheng Cui, Sasan Mahmoodi, Matthew Guy, Emma Lewis, Tom Havelock, Michael Bennet and Joy Conway
Type: Article
| 2020
| Item availability restricted.