1 citations · 2 across the 3 of their papers we have counts for
4 papers · 1 filter
Homology Features for X-Ray Hyperspectral Images Segmentation
Fethi Harkat, Guillaume Gey, Valérie Perrier +1
Characterizing Multi-Energy X-ray Transmission (XRT) hyperspectral images presents significant challenges for traditional classification frameworks due to high-dimensional redundan…
Manifold Learning for Hyperspectral Images
Fethi Harkat, Guillaume Gey, Valérie Perrier +2
Traditional feature extraction and projection techniques, such as Principal Component Analysis, struggle to adequately represent X-Ray Transmission (XRT) Multi-Energy (ME) images,…
From CNNs to Shift-Invariant Twin Models Based on Complex Wavelets
Hubert Leterme, Kévin Polisano, Valérie Perrier +1
We propose a novel method to increase shift invariance and prediction accuracy in convolutional neural networks. Specifically, we replace the first-layer combination "real-valued c…
On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks
Hubert Leterme, Kévin Polisano, Valérie Perrier +1
This paper focuses on improving the mathematical interpretability of convolutional neural networks (CNNs) in the context of image classification. Specifically, we tackle the instab…