Vision, Learning and Control

As part of the Department of Electronics and Computer Science, our research includes: 
 

Computer vision 

Our image processing and computer vision ranges from techniques in pre-processing, to feature extraction and image analysis. VLC researchers have a long record in biometrics, developing work in gait and facial recognition. 
 
VLC are developing soft biometrics, learning from human labelling to augment or even replace the automatically derived measures. We are also working in new areas related to deep convolutional neural networks and feature learning, which cross the boundary with machine learning. 

Machine learning 

Machine learning in our group covers a broad range of areas ranging from developing new classification and clustering tools for big data sets, to mathematical modelling of complex systems and optimisation.

Application areas include: 

• biological sequence analysis 
• gene regulation 
• text analysis 
• computer vision 
• recommender systems 
• combinatorial optimisation 

Control 

VLC researchers’ work 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 
• flow control 

They use a variety of techniques from linear algebra, functional analysis, partial differential equations and commutative algebra.