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
AI-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 £770,000 valuation for her deep learning tool for archaeological surveys in a Dragons Den-style event at the University of Southampton.
Read MoreRapid 3D face modelling explored for...
Computer vision experts at the University of Southampton have proposed using camera sensors to generate a 3D model of the face in a quick and efficient facemask fitting system.
Read MoreRecent VLC publications
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
| Item availability restricted.
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
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
Type: Thesis
| 2020
| University of Southampton
| Item availability restricted.