439 citations · 634 across the 13 of their papers we have counts for
14 papers
PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images
Stefano Zorzi, Shabab Bazrafkan, Stefan Habenschuss +1
While most state-of-the-art instance segmentation methods produce binary segmentation masks, geographic and cartographic applications typically require precise vector polygons of e…
Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets
Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran
StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples. In this paper, we recap the StyleGAN arc…
To Recurse or not to Recurse,a Low Dose CT Study
Shabab Bazrafkan, Vincent Van Nieuwenhove, Jan Sijbers
Restoring high-quality CT images from low dose CT counterparts is an ill-posed, nonlinear problem to which Deep Learning approaches have been giving superior solutions compared to…
Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT
Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2
Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exp…
Deep Learning Based Computed Tomography Whys and Wherefores
Shabab Bazrafkan, Vincent Van Nieuwenhove, Joris Soons +2
This is an article about the Computed Tomography (CT) and how Deep Learning influences CT reconstruction pipeline, especially in low dose scenarios.
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran
A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low comple…