activity
20212023
most citedFusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks

37 citations · 45 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2023★ 6 cited

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…

eess.AS2023★ 1 cited

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…

cs.CV2022★ 1 cited

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…

cs.LG2022

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…

cs.CV2021★ 37 cited

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…