1 citations · 2 across the 3 of their papers we have counts for
5 papers
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…
Riesz-based orientation of localizable Gaussian fields
Kévin Polisano, Marianne Clausel, Valérie Perrier +1
In this work we give a sense to the notion of orientation for self-similar Gaussian fields with stationary increments, based on a Riesz analysis of these fields, with isotropic zer…
Texture Modeling by Gaussian fields with prescribed local orientation
Kévin Polisano, Marianne Clausel, Valérie Perrier +1
This paper presents a new framework for oriented texture modeling. We introduce a new class of Gaussian fields, called Locally Anisotropic Fractional Brownian Fields, with prescrib…