Vision, learning and control
Our group explores the theory behind technological areas including control, machine learning and computer vision.
As part of the Department of Electronics and Computer Science, our research includes:
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 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:
VLC researchers’ work includes behavioural approaches to:
They use a variety of techniques from linear algebra, functional analysis, partial differential equations and commutative algebra.
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My research is at the intersection of machine learning and digital health. I am interested in developing machine learning methods for human sensing using vision- and wearable-based technologies.