Zobrazeno 1 - 10
of 1 385
pro vyhledávání: '"Poullis A"'
Extraction of building footprint polygons from remotely sensed data is essential for several urban understanding tasks such as reconstruction, navigation, and mapping. Despite significant progress in the area, extracting accurate polygonal building f
Externí odkaz:
http://arxiv.org/abs/2412.07899
Currently, there are no learning-free or neural techniques for real-time recalibration of infrared multi-camera systems. In this paper, we address the challenge of real-time, highly-accurate calibration of multi-camera infrared systems, a critical ta
Externí odkaz:
http://arxiv.org/abs/2410.14505
Hydrometric forecasting is crucial for managing water resources, flood prediction, and environmental protection. Water stations are interconnected, and this connectivity influences the measurements at other stations. However, the dynamic and implicit
Externí odkaz:
http://arxiv.org/abs/2409.15213
The hydrometric prediction of water quantity is useful for a variety of applications, including water management, flood forecasting, and flood control. However, the task is difficult due to the dynamic nature and limited data of water systems. Highly
Externí odkaz:
http://arxiv.org/abs/2312.05961
Recent advancements in deep learning and computer vision have led to widespread use of deep neural networks to extract building footprints from remote-sensing imagery. The success of such methods relies on the availability of large databases of high-
Externí odkaz:
http://arxiv.org/abs/2304.02296
Autor:
Naghmeh Shafiee Roudbari, Shubham Rajeev Punekar, Zachary Patterson, Ursula Eicker, Charalambos Poullis
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Forecasting floods encompasses significant complexity due to the nonlinear nature of hydrological systems, which involve intricate interactions among precipitation, landscapes, river systems, and hydrological networks. Recent efforts in hydr
Externí odkaz:
https://doaj.org/article/6ec87701f8174705a14ebdc8d037c8f0
Autor:
Chen, Qiao, Poullis, Charalambos
Image-based 3D reconstruction is one of the most important tasks in Computer Vision with many solutions proposed over the last few decades. The objective is to extract metric information i.e. the geometry of scene objects directly from images. These
Externí odkaz:
http://arxiv.org/abs/2209.06926
In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks. This is particularly the case for traffic forecasting, where GNN m
Externí odkaz:
http://arxiv.org/abs/2209.03858
Autor:
Shahfar, Shima, Poullis, Charalambos
The Swapping Autoencoder achieved state-of-the-art performance in deep image manipulation and image-to-image translation. We improve this work by introducing a simple yet effective auxiliary module based on gradient reversal layers. The auxiliary mod
Externí odkaz:
http://arxiv.org/abs/2208.11546
Autor:
Chen, Qiao, Poullis, Charalambos
Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour, gradient an
Externí odkaz:
http://arxiv.org/abs/2206.12464