Human Motion Analysis via Statistical Motion Processing and Sequential Change Detection

Autor: Alexia Briassouli, Vagia Tsiminaki, Ioannis Kompatsiaris

Publikováno v: EURASIP Journal on Image and Video Processing, Vol 2009 (2009)

The widespread use of digital multimedia in applications, such as security, surveillance, and the semantic web, has made the automated characterization of human activity necessary. In this work, a method for the characterization of multiple human act

Externí odkaz: https://doaj.org/article/ade99408943a4b9587aa2d3b9bedd27d

Deep learning-based super-resolution and de-noising for XMM-newton images

Autor: Sam F Sweere, Ivan Valtchanov, Maggie Lieu, Antonia Vojtekova, Eva Verdugo, Maria Santos-Lleo, Florian Pacaud, Alexia Briassouli, Daniel Cámpora Pérez

Publikováno v: Monthly Notices of the Royal Astronomical Society, 517(3), 4054-4069. Oxford University Press

The field of artificial intelligence based image enhancement has been rapidly evolving over the last few years and is able to produce impressive results on non-astronomical images. In this work, we present the first application of Machine Learning ba

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9a543969abe86b1aec9a67abe821523b
https://cris.maastrichtuniversity.nl/en/publications/69083ed2-9997-4140-8fb6-c5566df80326

Tracking Rapid Permafrost thaw Through Time: Exploring the Potential of Convolutional Neural Network based Models

Autor: Felix Rustemeyer, Julia Barrott, Matthew Fielding, Adam Wickenden, Gustaf Hugelius, Alexia Briassouli

Publikováno v: IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 3838-3841
STARTPAGE=3838;ENDPAGE=3841;TITLE=IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium

This paper presents the novel use of convolutional neural network (CNN)-based machine learning models for remotely detecting and monitoring retrogressive thaw slumps (RTS) in high latitude northern permafrost using open-source Sentinel-2 satellite da

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b7637c72ce989a2d3b347b9a5b9f29a
https://doi.org/10.1109/igarss46834.2022.9884869

Improving accuracy and efficiency in plant detection on a novel, benchmarking real-world dataset

Autor: Laurenz Ohnemuller, Alexia Briassouli

Publikováno v: 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), 172-176
STARTPAGE=172;ENDPAGE=176;TITLE=2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

Detecting plants in images is central in precision agriculture, but can be challenging due to their small size, similarities in appearance, varying lighting and environmental conditions. Moreover, computational capacity in real-world settings may be

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::baf0ba75cdb2657e3b552966f24e9c10
https://cris.maastrichtuniversity.nl/en/publications/f62c0d00-056b-46cf-a291-9a65c287910b

Mining discriminative descriptors for goal-based activity detection

Autor: Stavros Tachos, Alexia Briassouli, Konstantinos Avgerinakis, Ioannis Kompatsiaris

Publikováno v: Computer Vision and Image Understanding, 160, 73-86. Academic Press Inc.

Human activity detection from video that is recorded continuously over time has been gaining increasing attention due to its use in applications like security monitoring, smart homes and assisted living setups. The analysis of continuous videos for t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0d879096fbc49e79d0398e12f69296e
https://doi.org/10.1016/j.cviu.2017.04.001