18 citations · 33 across the 10 of their papers we have counts for
14 papers
MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation
Jiaying Li, Paolo Remagnino
Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic Euclidean lattice assumption.…
Content-aware Density Map for Crowd Counting and Density Estimation
Mahdi Maktabdar Oghaz, Anish R Khadka, Vasileios Argyriou +1
Precise knowledge about the size of a crowd, its density and flow can provide valuable information for safety and security applications, event planning, architectural design and to…
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation
Antoine Rimboux, Rob Dupre, Thomas Lagkas +3
Smart video sensors for applications related to surveillance and security are IOT-based as they use Internet for various purposes. Such applications include crowd behaviour monitor…
Iterative Self-Learning: Semi-Supervised Improvement to Dataset Volumes and Model Accuracy
Robert Dupre, Jiri Fajtl, Vasileios Argyriou +1
A novel semi-supervised learning technique is introduced based on a simple iterative learning cycle together with learned thresholding techniques and an ensemble decision support s…
Scene and Environment Monitoring Using Aerial Imagery and Deep Learning
Mahdi Maktabdar Oghaz, Manzoor Razaak, Hamideh Kerdegari +2
Unmanned Aerial vehicles (UAV) are a promising technology for smart farming related applications. Aerial monitoring of agriculture farms with UAV enables key decision-making pertai…
Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs
Hamideh Kerdegari, Manzoor Razaak, Vasileios Argyriou +1
Unmanned aerial vehicles (UAV) are used in precision agriculture (PA) to enable aerial monitoring of farmlands. Intelligent methods are required to pinpoint weed infestations and m…
A Comparison of Embedded Deep Learning Methods for Person Detection
Chloe Eunhyang Kim, Mahdi Maktab Dar Oghaz, Jiri Fajtl +2
Recent advancements in parallel computing, GPU technology and deep learning provide a new platform for complex image processing tasks such as person detection to flourish. Person d…
Summarizing Videos with Attention
Jiri Fajtl, Hajar Sadeghi Sokeh, Vasileios Argyriou +2
In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mecha…
Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering
Anish R. Khadka, Paolo Remagnino, Vasileios Argyriou
Methods for 3D reconstruction such as Photometric stereo recover the shape and reflectance properties using multiple images of an object taken with variable lighting conditions fro…
AMNet: Memorability Estimation with Attention
Jiri Fajtl, Vasileios Argyriou, Dorothy Monekosso +1
In this paper we present the design and evaluation of an end-to-end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We…
Superframes, A Temporal Video Segmentation
Hajar Sadeghi Sokeh, Vasileios Argyriou, Dorothy Monekosso +1
The goal of video segmentation is to turn video data into a set of concrete motion clusters that can be easily interpreted as building blocks of the video. There are some works on…
Deep-Plant: Plant Identification with convolutional neural networks
Sue Han Lee, Chee Seng Chan, Paul Wilkin +1
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, En…
Detection of Salient Regions in Crowded Scenes
Mei Kuan Lim, Chee Seng Chan, Dorothy Monekosso +1
The increasing number of cameras and a handful of human operators to monitor the video inputs from hundreds of cameras leave the system ill equipped to fulfil the task of detecting…
Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking
Mei Kuan Lim, Chee Seng Chan, Dorothy Monekosso +1
Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model. Abrupt motion is not subject t…