356 citations · 871 across the 17 of their papers we have counts for
19 papers
Event Detection in Videos: A Framework for the Development of New Methods
Anastasia Zakharova, Thierry Bouwmans, Anthony Cioppa +13
Event detection tasks in videos, the most important aspect of video surveillance, aim to detect events either at the pixel-level, frame-level, or clip-level. Plenty of methods inte…
Discovering Local Binary Pattern Equation for Foreground Object Removal in Videos
Caroline Pacheco do Espirito Silva, Andrews Cordolino Sobral, Antoine Vacavant +2
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 episode…
Inductive Graph Neural Networks for Moving Object Segmentation
Wieke Prummel, Jhony H. Giraldo, Anastasia Zakharova +1
Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and…
Higher-order Sparse Convolutions in Graph Neural Networks
Jhony H. Giraldo, Sajid Javed, Arif Mahmood +2
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…
Time-varying Signals Recovery via Graph Neural Networks
Jhon A. Castro-Correa, Jhony H. Giraldo, Anindya Mondal +3
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-t…
On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks
Jhony H. Giraldo, Konstantinos Skianis, Thierry Bouwmans +1
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…
Hypergraph Convolutional Networks for Weakly-Supervised Semantic Segmentation
Jhony H. Giraldo, Vincenzo Scarrica, Antonino Staiano +2
Semantic segmentation is a fundamental topic in computer vision. Several deep learning methods have been proposed for semantic segmentation with outstanding results. However, these…
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos
Jhony H. Giraldo, Sajid Javed, Naoufel Werghi +1
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 camer…
Reconstruction of Time-varying Graph Signals via Sobolev Smoothness
Jhony H. Giraldo, Arif Mahmood, Belmar Garcia-Garcia +2
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…
Moving Object Detection for Event-based vision using Graph Spectral Clustering
Anindya Mondal, Shashant R, Jhony H. Giraldo +2
Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and en…
Automated Mathematical Equation Structure Discovery for Visual Analysis
Caroline Pacheco do Espírito Silva, José A. M. Felippe De Souza, Antoine Vacavant +2
Finding the best mathematical equation to deal with the different challenges found in complex scenarios requires a thorough understanding of the scenario and a trial and error proc…
On the Minimization of Sobolev Norms of Time-Varying Graph Signals: Estimation of New Coronavirus Disease 2019 Cases
Jhony H. Giraldo, Thierry Bouwmans
The mathematical modeling of infectious diseases is a fundamental research field for the planning of strategies to contain outbreaks. The models associated with this field of study…
Moving Objects Detection with a Moving Camera: A Comprehensive Review
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. First applications concern stati…
GraphBGS: Background Subtraction via Recovery of Graph Signals
Jhony H. Giraldo, Thierry Bouwmans
Background subtraction is a fundamental pre-processing task in computer vision. This task becomes challenging in real scenarios due to variations in the background for both static…
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
T. Bouwmans, B. Garcia-Garcia
Computer vision applications based on videos often require the detection of moving objects in their first step. Background subtraction is then applied in order to separate the back…
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
Thierry Bouwmans, Sajid Javed, Maryam Sultana +1
Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on…
Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery
Namrata Vaswani, Thierry Bouwmans, Sajid Javed +1
PCA is one of the most widely used dimension reduction techniques. A related easier problem is "subspace learning" or "subspace estimation". Given relatively clean data, both are e…
On the Role and the Importance of Features for Background Modeling and Foreground Detection
Thierry Bouwmans, Caroline Silva, Cristina Marghes +3
Background modeling has emerged as a popular foreground detection technique for various applications in video surveillance. Background modeling methods have become increasing effic…
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
Thierry Bouwmans, Andrews Sobral, Sajid Javed +2
Recent research on problem formulations based on decomposition into low-rank plus sparse matrices shows a suitable framework to separate moving objects from the background. The mos…