18 citations · 23 across the 4 of their papers we have counts for
7 papers
Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose
Roderick Murray-Smith, John H. Williamson, Andrew Ramsay +3
We outline the role of forward and inverse modelling approaches in the design of human--computer interaction systems. Causal, forward models tend to be easier to specify and simula…
The role of late photons in diffuse optical imaging
Jack Radford, Ashley Lyons, Francesco Tonolini +1
The ability to image through turbid media such as organic tissues, is a highly attractive prospect for biological and medical imaging. This is challenging however, due to the highl…
Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Francesco Tonolini, Pablo G. Moreno, Andreas Damianou +1
We propose a new probabilistic method for unsupervised recovery of corrupted data. Given a large ensemble of degraded samples, our method recovers accurate posteriors of clean valu…
Spatial images from temporal data
Alex Turpin, Gabriella Musarra, Valentin Kapitany +8
Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial st…
Variational Inference for Computational Imaging Inverse Problems
Francesco Tonolini, Jack Radford, Alex Turpin +2
Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of re…
Computational time-of-flight diffuse optical tomography
Ashley Lyons, Francesco Tonolini, Alessandro Boccolini +4
Imaging through a strongly diffusive medium remains an outstanding challenge in particular in association with applications in biological and medical imaging. Here we propose a met…