Mathematical morphology provides powerful nonlinear operators for a variety of image
processing tasks such as filtering, segmentation, and edge detection. In this paper, we propose a …

The latest Deep Learning (DL) models for detection and classification have achieved an
unprecedented performance over classical machine learning algorithms. However, DL models …

During training, the weights of a Deep Neural Network (DNN) are optimized from a random
initialization towards a nearly optimum value minimizing a loss function. Only this final state …

Although Deep Neural Networks (DNNs) have great generalization and prediction capabilities,
their functioning does not allow a detailed explanation of their behavior. Opaque deep …

This article deals with the issue of reducing the spectral dimension of a hyperspectral image
using principal component analysis (PCA). To perform this dimensionality reduction, we …

The capacity of a modern deep learning system to determine if a sample falls within its realm
of knowledge is fundamental and important. In this paper, we offer insights and analyses of …

‘Hydrogen as the energy carrier of the future’ has been a topic discussed for decades and is
today the subject of a new revival, especially driven by the investments in renewable …

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world
autonomous systems. However, most successful approaches are computationally …

Predictive uncertainty estimation is essential for safe deployment of Deep Neural Networks in
real-world autonomous systems. However, disentangling the different types and sources of …

Deep Ensembles (DE) are a prominent approach for achieving excellent performance on
key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution …