301 citations · 346 across the 6 of their papers we have counts for
6 papers
SAMBA: A Trainable Segmentation Web-App with Smart Labelling
Ronan Docherty, Isaac Squires, Antonis Vamvakeros +1
Segmentation is the assigning of a semantic class to every pixel in an image and is a prerequisite for various statistical analysis tasks in materials science, like phase quantific…
HRTF upsampling with a generative adversarial network using a gnomonic equiangular projection
Aidan O. T. Hogg, Mads Jenkins, He Liu +3
An individualised head-related transfer function (HRTF) is very important for creating realistic virtual reality (VR) and augmented reality (AR) environments. However, acoustically…
Two approaches to inpainting microstructure with deep convolutional generative adversarial networks
Isaac Squires, Samuel J. Cooper, Amir Dahari +1
Imaging is critical to the characterisation of materials. However, even with careful sample preparation and microscope calibration, imaging techniques are often prone to defects an…
MicroLib: A library of 3D microstructures generated from 2D micrographs using SliceGAN
Steve Kench, Isaac Squires, Amir Dahari +1
3D microstructural datasets are commonly used to define the geometrical domains used in finite element modelling. This has proven a useful tool for understanding how complex materi…
Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks
Amir Dahari, Steve Kench, Isaac Squires +1
Modelling the impact of a material's mesostructure on device level performance typically requires access to 3D image data containing all the relevant information to define the geom…
Generating 3D structures from a 2D slice with GAN-based dimensionality expansion
Steve Kench, Samuel J. Cooper
Generative adversarial networks (GANs) can be trained to generate 3D image data, which is useful for design optimisation. However, this conventionally requires 3D training data, wh…