Charlie Nash

I'm a PhD student at the University of Edinburgh supervised by Chris Williams. I'm part of the Centre of Doctoral Training in Data Science.

My interests are in probabilistic modelling, computer vision and deep learning. Currently my research is focused on 3D shape modelling for computer vision applications.

CV

Publications


Generative models of part-structured 3D objects.
Charlie Nash and Chris Williams, Neural Information Processing Systems (NIPS), 3D Deep Learning Workshop, 2016.
pdf bibtex

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.
pdf bibtex

Projects


Modelling 3D Shape Classes

Distilling Model Knowledge

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.

This work was my MSc project. More info can be found in the poster and MSc thesis.

Classifying Tasks Using a Brain-Computer Interface

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.

This was a short project as part of the MSc in Data Science at Edinburgh University. More info can be found in the poster and report.

Code


Optimal step non-rigid ICP

Distilling Model Knowledge

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.

Features:

  • Non-rigid and local deformations of a template surface or point cloud.
  • Iterative stiffness reduction allows for global intitial transformations that become increasingly localised.
  • Optional initial rigid registration using standard iterative closest point.
  • Optional bi-directional distance metric which encourages surface deformations to cover more of the target surface

pyMM

Distilling Model Knowledge

Python implementation of Gaussian Mixture Model (GMM) variants.

Features:

  • GMMs with full, diagonal and spherical covariance matrices.
  • Mixture of factor analysers (MFA) and mixture of probabilistic principal component analysis models (MPPCA).
  • Handles missing data using the EM algorithm.

Bio


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.

Contact


Email
charlie.nash@ed.ac.uk

Address
Room 2.25, Informatics Forum
10 Crichton Street
Edinburgh, UK
EH8 8BQ