13 citations · 23 across the 4 of their papers we have counts for
8 papers
Variational Osmosis for Non-linear Image Fusion
Simone Parisotto, Luca Calatroni, Aurélie Bugeau +2
We propose a new variational model for non-linear image fusion. Our approach is based on the use of an osmosis energy term related to the one studied in Vogel et al. (2013) and Wei…
GraphX Chest X-Ray Classification Under Extreme Minimal Supervision
Angelica I. Aviles-Rivero, Nicolas Papadakis, Ruoteng Li +4
The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst supervised deep learning methods rely upon huge amounts of labelled data, the crit…
Learning to segment microscopy images with lazy labels
Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis +2
The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instan…
Refitting solutions promoted by sparse analysis regularization with block penalties
Charles-Alban Deledalle, Nicolas Papadakis, Joseph Salmon +1
In inverse problems, the use of an analysis regularizer induces a bias in the estimated solution. We propose a general refitting framework for removing this artifact wh…
Approximation of Wasserstein distance with Transshipment
Nicolas Papadakis
An algorithm for approximating the p-Wasserstein distance between histograms defined on unstructured discrete grids is presented. It is based on the computation of a barycenter con…
Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis +3
In this paper, we present a graph-based semi-supervised framework for hyperspectral image classification. We first introduce a novel superpixel algorithm based on the spectral cova…