The University of Southampton

Computer Vision

Our research in image processing and computer vision spans techniques from preprocessing, to feature extraction (especially moving ones) and on to image analysis. Our approaches to feature extraction have extended classic technique, such as active contours/ level sets and the Hough transform, and we have started totally new approaches. The new approaches have been phrased around using analogies such as water, heat and light. These analogies simplify the problems and enable alternative methods of feature extraction. Our main application areas have been in biometrics, in remote sensing and in medical image analysis. We have a long record in biometrics, starting in automatic face recognition and have since conducted some of the earliest work in recognising people by their gait and by their ears. We continue to work in these areas and are now developing soft biometrics, in which we learn from human labelling to augment or even replace the automatically derived measures.

Machine Learning

Machine learning in VLC covers a broad range of areas ranging from developing new classification and clustering tools for big data sets, mathematical modelling of complex systems and optimisation.  Application areas include biological sequence analysis, gene regulation, text analysis, recommender systems, combinatorial optimisation, etc.


Our work on fundamental theory, includes behavioural approaches to system theory; system identification particularly using structured low-rank approximations; multidimensional systems theory; robust nonlinear control; iterative learning control; adaptive control and flow control. We use a variety of techniques from linear algebra, functional analysis, partial differential equations and commutative algebra.