Zobrazeno 1 - 10
of 111
pro vyhledávání: '"Jan Aelterman"'
Publikováno v:
Applied Sciences, Vol 14, Iss 10, p 4081 (2024)
Recent advancements in high dynamic range (HDR) display technology have significantly enhanced the contrast ratios and peak brightness of modern displays. In the coming years, it is expected that HDR televisions capable of delivering significantly hi
Externí odkaz:
https://doaj.org/article/ceda8f6ce2564d8bbec16d87e195bf65
Autor:
Noémie Johnston, Jeffrey De Rycke, Yolande Lievens, Marc van Eijkeren, Jan Aelterman, Eva Vandersmissen, Stephan Ponte, Barbara Vanderstraeten
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 23, Iss , Pp 109-117 (2022)
Background and purpose: The geometrical accuracy of auto-segmentation using convolutional neural networks (CNNs) has been demonstrated. This study aimed to investigate the dose-volume impact of differences between automatic and manual OARs for locall
Externí odkaz:
https://doaj.org/article/e7bfeb5bed11459dba361fc568572472
Publikováno v:
Sensors, Vol 23, Iss 20, p 8507 (2023)
High dynamic range (HDR) imaging technology is increasingly being used in automated driving systems (ADS) for improving the safety of traffic participants in scenes with strong differences in illumination. Therefore, a combination of HDR video, that
Externí odkaz:
https://doaj.org/article/65ab417d052746099670f43042415e6c
Autor:
Benjamin K. Blykers, Caori Organista, Matthieu N. Boone, Matias Kagias, Federica Marone, Marco Stampanoni, Tom Bultreys, Veerle Cnudde, Jan Aelterman
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
Abstract X-ray computed micro-tomography typically involves a trade-off between sample size and resolution, complicating the study at a micrometer scale of representative volumes of materials with broad feature size distributions (e.g. natural stones
Externí odkaz:
https://doaj.org/article/f63e8cbd0e7d4d7a9019b6ff971d071d
Autor:
Zaira Manigrasso, Wannes Goethals, Niloofar M. Goudarzi, Matthieu N. Boone, Aseel Samaro, Chris Vervaet, Wilfried Philips, Jan Aelterman
Publikováno v:
Frontiers in Materials, Vol 9 (2022)
Digital volume correlation (DVC) is a 3D image-based technique for displacement and strain computation. Traditionally, both (digital image correlation) DIC and DVC are methods based on two individual time frames; the estimation of the displacement an
Externí odkaz:
https://doaj.org/article/593f591d24044d78ab1718c65682f8df
Publikováno v:
Sensors, Vol 23, Iss 12, p 5767 (2023)
Intelligent driver assistance systems are becoming increasingly popular in modern passenger vehicles. A crucial component of intelligent vehicles is the ability to detect vulnerable road users (VRUs) for an early and safe response. However, standard
Externí odkaz:
https://doaj.org/article/3078cec522564a53b502ccf90b9a5aea
Autor:
Zaira Manigrasso, Wannes Goethals, Pierre Kibleur, Matthieu N. Boone, Wilfried Philips, Jan Aelterman
Publikováno v:
Applied Sciences, Vol 13, Iss 12, p 6980 (2023)
Introduction: Accurately detecting cracks is crucial for assessing the health of materials. Manual detection methods are time-consuming, leading to the development of automatic detection techniques based on image processing and machine learning. Thes
Externí odkaz:
https://doaj.org/article/158fd65e94554f559fb7579fb0ae7b93
Publikováno v:
IEEE Access, Vol 9, Pp 43938-43969 (2021)
We propose a novel and efficient algorithm for detection of specular reflections and light sources (highlights) in cinematic content. The detection of highlights is important for reconstructing them properly in the conversion of the low dynamic range
Externí odkaz:
https://doaj.org/article/6850794752ec483b84faa9cda80fc0a1
Autor:
Joris Roels, Frank Vernaillen, Anna Kremer, Amanda Gonçalves, Jan Aelterman, Hiêp Q. Luong, Bart Goossens, Wilfried Philips, Saskia Lippens, Yvan Saeys
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Large 3D electron microscopy data sets frequently contain noisy data due to accelerated imaging, and denoising techniques require specialised skill sets. Here the authors introduce DenoisEM, an ImageJ plugin that democratises denoising EM data sets,
Externí odkaz:
https://doaj.org/article/f017d27164324c11a77f3a68f98b1af1
Publikováno v:
Materials, Vol 15, Iss 22, p 8168 (2022)
We would like to change the authors’ affiliation on the recent published paper [...]
Externí odkaz:
https://doaj.org/article/c66b61e53e70492bb731412f2c86cc39