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
Rapid 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 MoreScientists 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 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
| 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
Type: Thesis
| 2020
| University of Southampton
| Item availability restricted.