67 citations · 292 across the 17 of their papers we have counts for
17 papers
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
Ahmed Rida Sekkat, Yohan Dupuis, Varun Ravi Kumar +4
Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used…
Effect of Prior-based Losses on Segmentation Performance: A Benchmark
Rosana El Jurdi, Caroline Petitjean, Veronika Cheplygina +2
Today, deep convolutional neural networks (CNNs) have demonstrated state-of-the-art performance for medical image segmentation, on various imaging modalities and tasks. Despite ear…
Breaking the Limits of Message Passing Graph Neural Networks
Muhammet Balcilar, Pierre Héroux, Benoit Gaüzère +3
Since the Message Passing (Graph) Neural Networks (MPNNs) have a linear complexity with respect to the number of nodes when applied to sparse graphs, they have been widely implemen…
High-level Prior-based Loss Functions for Medical Image Segmentation: A Survey
Rosana El Jurdi, Caroline Petitjean, Paul Honeine +2
Today, deep convolutional neural networks (CNNs) have demonstrated state of the art performance for supervised medical image segmentation, across various imaging modalities and tas…
Statistical learning for sensor localization in wireless networks
Daniel Alshamaa, Farah Chehade, Paul Honeine
Indoor localization has become an important issue for wireless sensor networks. This paper presents a zoning-based localization technique that uses WiFi signals and works efficient…
Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks
Muhammet Balcilar, Guillaume Renton, Pierre Heroux +3
This paper aims at revisiting Graph Convolutional Neural Networks by bridging the gap between spectral and spatial design of graph convolutions. We theoretically demonstrate some e…
Maximum Correntropy Unscented Filter
Xi Liu, Badong Chen, Bin Xu +2
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximati…
Bayesian Filtering of Smooth Signals: Application to Altimetry
Abderrahim Halimi, Gerald S. Buller, Steve McLaughlin +1
This paper presents a novel Bayesian strategy for the estimation of smooth signals corrupted by Gaussian noise. The method assumes a smooth evolution of a succession of continuous…
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
Fei Zhu, Abderrahim Halimi, Paul Honeine +2
In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixi…
Hyperspectral Unmixing in Presence of Endmember Variability, Nonlinearity or Mismodelling Effects
Abderrahim Halimi, Paul Honeine, Jose Bioucas-Dias
This paper presents three hyperspectral mixture models jointly with Bayesian algorithms for supervised hyperspectral unmixing. Based on the residual component analysis model, the p…
Bi-Objective Nonnegative Matrix Factorization: Linear Versus Kernel-Based Models
Paul Honeine, Fei Zhu
Nonnegative matrix factorization (NMF) is a powerful class of feature extraction techniques that has been successfully applied in many fields, namely in signal and image processing…
Estimating the Intrinsic Dimension of Hyperspectral Images Using an Eigen-Gap Approach
A. Halimi, P. Honeine, M. Kharouf +2
Linear mixture models are commonly used to represent hyperspectral datacube as a linear combinations of endmember spectra. However, determining of the number of endmembers for imag…
Entropy of Overcomplete Kernel Dictionaries
Paul Honeine
In signal analysis and synthesis, linear approximation theory considers a linear decomposition of any given signal in a set of atoms, collected into a so-called dictionary. Relevan…
Approximation errors of online sparsification criteria
Paul Honeine
Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification schem…
Analyzing sparse dictionaries for online learning with kernels
Paul Honeine
Many signal processing and machine learning methods share essentially the same linear-in-the-parameter model, with as many parameters as available samples as in kernel-based machin…
An eigenanalysis of data centering in machine learning
Paul Honeine
Many pattern recognition methods rely on statistical information from centered data, with the eigenanalysis of an empirical central moment, such as the covariance matrix in princip…
Kernel Nonnegative Matrix Factorization Without the Curse of the Pre-image - Application to Unmixing Hyperspectral Images
Fei Zhu, Paul Honeine, Maya Kallas
The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in rem…