no code implementations • 11 Aug 2023 • Caroline Pacheco do Espirito Silva, Andrews Cordolino Sobral, Antoine Vacavant, Thierry Bouwmans, Felippe De Souza
Designing a novel Local Binary Pattern (LBP) process usually relies heavily on human experts' knowledge and experience in the area.
no code implementations • 16 May 2023 • Wieke Prummel, Jhony H. Giraldo, Anastasia Zakharova, Thierry Bouwmans
Our proposed algorithm enables the deployment of graph-based MOS models in real-world applications.
no code implementations • 22 Feb 2023 • Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal, Mohsen Badiey, Thierry Bouwmans, Fragkiskos D. Malliaros
The recovery of time-varying graph signals is a fundamental problem with numerous applications in sensor networks and forecasting in time series.
no code implementations • 21 Feb 2023 • Jhony H. Giraldo, Sajid Javed, Arif Mahmood, Fragkiskos D. Malliaros, Thierry Bouwmans
Graph Neural Networks (GNNs) have been applied to many problems in computer sciences.
1 code implementation • 5 Dec 2022 • Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans, Fragkiskos D. Malliaros
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.
no code implementations • 11 Oct 2022 • Jhony H. Giraldo, Vincenzo Scarrica, Antonino Staiano, Francesco Camastra, Thierry Bouwmans
Our algorithm constructs spatial and k-Nearest Neighbor (k-NN) graphs from the images in the dataset to generate the hypergraphs.
no code implementations • 13 Jul 2022 • Jhony H. Giraldo, Sajid Javed, Naoufel Werghi, Thierry Bouwmans
Moving Object Detection (MOD) is a fundamental step for many computer vision applications.
1 code implementation • 13 Jul 2022 • Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia, Dorina Thanou, Thierry Bouwmans
In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples.
1 code implementation • International Conference on Computer Vision Workshops 2021 • Anindya Mondal, Shashant R, Jhony H. Giraldo, Thierry Bouwmans, Ananda S. Chowdhury
However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution.
Ranked #1 on Moving Object Detection on DVSMOTION20
3 code implementations • 17 Apr 2021 • Caroline Pacheco do Espírito Silva, José A. M. Felippe De Souza, Antoine Vacavant, Thierry Bouwmans, Andrews Cordolino Sobral
In this paper, we focus on recent AI advances to present a novel framework for automatically discovering equations from scratch with little human intervention to deal with the different challenges encountered in real-world scenarios.
1 code implementation • 1 Jul 2020 • Jhony H. Giraldo, Thierry Bouwmans
To this end, we proposed a new method based on the minimization of the Sobolev norm in graph signal processing.
no code implementations • 17 Jan 2020 • Jhony H. Giraldo, Thierry Bouwmans
Several deep learning methods for background subtraction have been proposed in the literature with competitive performances.
2 code implementations • 15 Jan 2020 • Marie-Neige Chapel, Thierry Bouwmans
During about 30 years, a lot of research teams have worked on the big challenge of detection of moving objects in various challenging environments.
no code implementations • 13 Nov 2018 • Thierry Bouwmans, Sajid Javed, Maryam Sultana, Soon Ki Jung
Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies.
no code implementations • 26 Nov 2017 • Namrata Vaswani, Thierry Bouwmans, Sajid Javed, Praneeth Narayanamurthy
The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA).
no code implementations • 28 Nov 2016 • Thierry Bouwmans, Caroline Silva, Cristina Marghes, Mohammed Sami Zitouni, Harish Bhaskar, Carl Frelicot
Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance.
1 code implementation • 4 Nov 2015 • Thierry Bouwmans, Andrews Sobral, Sajid Javed, Soon Ki Jung, El-Hadi Zahzah
In this context, this work aims to initiate a rigorous and comprehensive review of the similar problem formulations in robust subspace learning and tracking based on decomposition into low-rank plus additive matrices for testing and ranking existing algorithms for background/foreground separation.