Zobrazeno 1 - 10
of 759
pro vyhledávání: '"Bouwmans, P."'
Autor:
Giraldo, Jhony H., Einizade, Aref, Todorovic, Andjela, Castro-Correa, Jhon A., Badiey, Mohsen, Bouwmans, Thierry, Malliaros, Fragkiskos D.
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks, 2024
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the
Externí odkaz:
http://arxiv.org/abs/2411.04570
Autor:
Benabdesselam, Mourad, Bahout, J., Mady, Franck, Blanc, Wilfried, Hamzaoui, Hicham El, Cassez, Andy, Delplace-Baudelle, Karen, Habert, Remi, Bouwmans, Geraud, Bouazaoui, Mohamed, Capoen, Bruno
Publikováno v:
IEEE Sensors Journal, 2021, 21 (24), pp.1-8
Two rods made from sol-gel silica have been doped with Ce ions or co-doped with Ce and Tb ions respectively. First, a thermoluminescence (TL) characterization of the trapping and luminescence parameters is carried out to understand the physical mecha
Externí odkaz:
http://arxiv.org/abs/2407.12364
Autor:
Silva, Caroline Pacheco do Espirito, Sobral, Andrews Cordolino, Vacavant, Antoine, Bouwmans, Thierry, De Souza, Felippe
Designing a novel Local Binary Pattern (LBP) process usually relies heavily on human experts' knowledge and experience in the area. Even experts are often left with tedious episodes of trial and error until they identify an optimal LBP for a particul
Externí odkaz:
http://arxiv.org/abs/2308.06305
Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results
Externí odkaz:
http://arxiv.org/abs/2305.09585
Autor:
Castro-Correa, Jhon A., Giraldo, Jhony H., Mondal, Anindya, Badiey, Mohsen, Bouwmans, Thierry, Malliaros, Fragkiskos D.
The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series. Effectively capturing the spatio-temporal information in these signals is essential for the downstream t
Externí odkaz:
http://arxiv.org/abs/2302.11313
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences. Capturing higher-order relationships between nodes is crucial to increase the expressive power of GNNs. However, existing methods to capture these relationships cou
Externí odkaz:
http://arxiv.org/abs/2302.10505
Graph Neural Networks (GNNs) have succeeded in various computer science applications, yet deep GNNs underperform their shallow counterparts despite deep learning's success in other domains. Over-smoothing and over-squashing are key challenges when st
Externí odkaz:
http://arxiv.org/abs/2212.02374
Autor:
Giraldo, Jhony H., Scarrica, Vincenzo, Staiano, Antonino, Camastra, Francesco, Bouwmans, Thierry
Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these models require a lot of densely annotated images. To address this pro
Externí odkaz:
http://arxiv.org/abs/2210.05564
Publikováno v:
Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 225-233
Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds
Externí odkaz:
http://arxiv.org/abs/2207.06440
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks, vol. 8, pp. 201-214, 2022
Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampli
Externí odkaz:
http://arxiv.org/abs/2207.06439