The University of Southampton

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. 

  • 30 July 2020

    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.

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  • 14 December 2018

    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.

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  • 7 February 2018

    Centre 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

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  • Publications archive

    Recent 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
    Improving object detection performance by lightweight approaches - Yingwei Zhou
    Type: Thesis | 2020 | University of Southampton | Item availability restricted.