My interests are in probabilistic modelling, computer vision and deep learning. Currently my research is focused on 3D shape modelling for computer vision applications.
Overcoming Occlusion with Inverse Graphics.
Pol Moreno, Chris Williams, Charlie Nash and Pushmeet Kohli, European Conference of Computer Vision (ECCV), Geometry meets Deep Learning Workshop, 2016.
A model of object shape can be very useful for computer vision applications, whether as a means of generating richly-annotated training data for a recognition model, or as a component in an inverse-graphics system.
In this project we develop a system that can generate novel instances of an object class, using a collection of examples from that object class as training data. Our system establishes correspondences between landmark points on different objects, learns a generative model of the point locations, samples landmark points using the model and then meshes the shape samples.
Electroencephalograms (EEGs) can record electrical activity in the brain. In conjunction with a brain-computer interface (BCI) they can be used to augment human sensory functions or control robotic devices.
In this project we use EEG inputs to classify which of three tasks a subject is performing. We apply feature selection, then use Random Forests with temporal smoothing to classify each time point.
MATLAB implementation of a non-rigid variant of the iterative closest point algorithm. It can be used to register 3D surfaces or point-clouds. The method is described in the following paper:
'Optimal Step Nonrigid ICP Algorithms for Surface Registration', Amberg, Romandhani and Vetter, CVPR, 2007.
Python implementation of Gaussian Mixture Model (GMM) variants.
Before starting my PhD, I did a Masters by Research in Data Science (as part of the CDT in Data Science) at Edinburgh. My MSc thesis was on 3D shape modelling and was supervised by Chris Williams.
Prior to that, I did my undergraduate degree also at Edinburgh where I studied Mathematics.