58 citations · 175 across the 25 of their papers we have counts for
26 papers
StraightPCF: Straight Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +3
Point cloud filtering is a fundamental 3D vision task, which aims to remove noise while recovering the underlying clean surfaces. State-of-the-art methods remove noise by moving no…
Weighted Point Cloud Normal Estimation
Weijia Wang, Xuequan Lu, Di Shao +4
Existing normal estimation methods for point clouds are often less robust to severe noise and complex geometric structures. Also, they usually ignore the contributions of different…
Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning
Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly +1
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progr…
IterativePFN: True Iterative Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Zhiwen Shao +3
The quality of point clouds is often limited by noise introduced during their capture process. Consequently, a fundamental 3D vision task is the removal of noise, known as point cl…
On the Behaviour of Pulsed Qubits and their Application to Feed Forward Networks
Matheus Moraes Hammes, Antonio Robles-Kelly
In the last two decades, the combination of machine learning and quantum computing has been an ever-growing topic of interest but, to this date, the limitations of quantum computin…
DGD-cGAN: A Dual Generator for Image Dewatering and Restoration
Salma Gonzalez-Sabbagh, Antonio Robles-Kelly, Shang Gao
Underwater images are usually covered with a blue-greenish colour cast, making them distorted, blurry or low in contrast. This phenomenon occurs due to the light attenuation given…
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation
Tariq M. Khan, Muhammad Arsalan, Antonio Robles-Kelly +1
Image segmentation is an important task in medical imaging. It constitutes the backbone of a wide variety of clinical diagnostic methods, treatments, and computer-aided surgeries.…
Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction
Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly +1
Compression of convolutional neural network models has recently been dominated by pruning approaches. A class of previous works focuses solely on pruning the unimportant filters to…
Graph Classification via Discriminative Edge Feature Learning
Yang Yi, Xuequan Lu, Shang Gao +2
Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when a…
A Capsule Network for Hierarchical Multi-Label Image Classification
Khondaker Tasrif Noor, Antonio Robles-Kelly, Brano Kusy
Image classification is one of the most important areas in computer vision. Hierarchical multi-label classification applies when a multi-class image classification problem is arran…
Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering
Dasith de Silva Edirimuni, Xuequan Lu, Gang Li +1
Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and o…
Incorporating the Barzilai-Borwein Adaptive Step Size into Sugradient Methods for Deep Network Training
Antonio Robles-Kelly, Asef Nazari
In this paper, we incorporate the Barzilai-Borwein step size into gradient descent methods used to train deep networks. This allows us to adapt the learning rate using a two-point…
RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation
Tariq M Khan, Antonio Robles-Kelly, Syed S. Naqvi
Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchm…
Robust Neural Regression via Uncertainty Learning
Akib Mashrur, Wei Luo, Nayyar A. Zaidi +1
Deep neural networks tend to underestimate uncertainty and produce overly confident predictions. Recently proposed solutions, such as MC Dropout and SDENet, require complex trainin…
Deep Point Cloud Normal Estimation via Triplet Learning
Weijia Wang, Xuequan Lu, Dasith de Silva Edirimuni +2
Normal estimation on 3D point clouds is a fundamental problem in 3D vision and graphics. Current methods often show limited accuracy in predicting normals at sharp features (e.g.,…
A Derivative-free Method for Quantum Perceptron Training in Multi-layered Neural Networks
Tariq M. Khan, Antonio Robles-Kelly
In this paper, we present a gradient-free approach for training multi-layered neural networks based upon quantum perceptrons. Here, we depart from the classical perceptron and the…
Feature Extraction Functions for Neural Logic Rule Learning
Shashank Gupta, Antonio Robles-Kelly, Mohamed Reda Bouadjenek
Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output. In this paper, we propose feature extracting functions for integra…
Toward a Deep Learning-Driven Intrusion Detection Approach for Internet of Things
Mengmeng Ge, Naeem Firdous Syed, Xiping Fu +2
Internet of Things (IoT) has brought along immense benefits to our daily lives encompassing a diverse range of application domains that we regularly interact with, ranging from hea…
Deep Patch-based Human Segmentation
Dongbo Zhang, Zheng Fang, Xuequan Lu +4
3D human segmentation has seen noticeable progress in re-cent years. It, however, still remains a challenge to date. In this paper, weintroduce a deep patch-based method for 3D hum…
Hierarchically Fair Federated Learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly +1
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the…
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Cheng Li, Sunil Gupta, Santu Rana +3
Scientific experiments are usually expensive due to complex experimental preparation and processing. Experimental design is therefore involved with the task of finding the optimal…
Super-resolved Chromatic Mapping of Snapshot Mosaic Image Sensors via a Texture Sensitive Residual Network
Mehrdad Shoeiby, Lars Petersson, Mohammad Ali Armin +2
This paper introduces a novel method to simultaneously super-resolve and colour-predict images acquired by snapshot mosaic sensors. These sensors allow for spectral images to be ac…
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
Mehrdad Shoeiby, Antonio Robles-Kelly, Ran Wei +1
This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution. The dataset was first released and…
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images
Hao Zhu, Mantang Guo, Hongdong Li +2
Light-field cameras (LFC) have received increasing attention due to their wide-spread applications. However, current LFCs suffer from the well-known spatio-angular trade-off, which…