Nicolas Passat
University of Reims Champagne-Ardenne
176 Papers
875 Citations
Nicolas Passat is an academic researcher from University of Reims Champagne-Ardenne. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 26, co-authored 157 publications. Previous affiliations of Nicolas Passat include University of Strasbourg & Centre national de la recherche scientifique.
Chat about Author
Papers
Multiscale brain MRI super-resolution using deep 3D convolutional networks.
Chi-Hieu Pham,Carlos Tor-Díez,Hélène Meunier,Nathalie Bednarek,Ronan Fablet,Nicolas Passat,François Rousseau +6 more
TL;DR: This work delves into the relevance of several factors in the performance of the purely convolutional neural network-based techniques for the monomodal super-resolution, and highlights that one single network can efficiently handle multiple arbitrary scaling factors based on a multiscale training approach.
154
A non-local fuzzy segmentation method: Application to brain MRI
TL;DR: Experiments performed on both synthetic and real MRI data, leading to the classification of brain tissues into grey matter, white matter and cerebrospinal fluid, indicate a significant improvement in performance in the case of higher noise levels, when compared to a range of standard algorithms.
129
Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology
TL;DR: A hierarchical approach is proposed to progressively extract segments of interest from the lowest to the highest resolution data, and then finally determine urban patterns from VHSR images, inspired by the principle of photo-interpretation.
99
Hierarchical extraction of landslides from multiresolution remotely sensed optical images
Camille Kurtz,André Stumpf,Jean-Philippe Malet,Pierre Gançarski,Anne Puissant,Nicolas Passat +5 more
TL;DR: This work proposes a hybrid segmentation/classification region-based method, which uses images of the same area at various spatial resolutions (Medium to Very High Resolution), and relies on a recently introduced top-down hierarchical framework for landslide analysis.
77
Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology.
Alice Dufour,Olena Tankyevych,Benoît Naegel,Hugues Talbot,Christian Ronse,Joseph Baruthio,Petr Dokládal,Nicolas Passat +7 more
TL;DR: New vessel segmentation and filtering techniques, relying on recent advances on spatially variant mathematical morphology and connected filtering are presented, and included in an angiographic data processing framework.
75