180 citations · 394 across the 17 of their papers we have counts for
19 papers
To what extent can current French mobile network support agricultural robots?
Pierre La Rocca, Gaël Guennebaud, Aurélie Bugeau
The large-scale integration of robots in agriculture offers many promises for enhancing sustainability and increasing food production. The numerous applications of agricultural rob…
Estimating The Carbon Footprint Of Digital Agriculture Deployment: A Parametric Bottom-Up Modelling Approach
Pierre La Rocca, Gaël Guennebaud, Aurélie Bugeau +1
Digitalization appears as a lever to enhance agriculture sustainability. However, existing works on digital agriculture's own sustainability remain scarce, disregarding the environ…
How digital will the future be? Analysis of prospective scenarios
Aurélie Bugeau, Anne-Laure Ligozat
With the climate change context, many prospective studies, generally encompassing all areas of society, imagine possible futures to expand the range of options. The role of digital…
How to estimate carbon footprint when training deep learning models? A guide and review
Lucia Bouza Heguerte, Aurélie Bugeau, Loïc Lannelongue
Machine learning and deep learning models have become essential in the recent fast development of artificial intelligence in many sectors of the society. It is now widely acknowled…
Assessing VoD pressure on network power consumption
Gaël Guennebaud, Aurélie Bugeau, Antoine Dudouit
Assessing the energy consumption or carbon footprint of data distribution of video streaming services is usually carried out through energy or carbon intensity figures (in Wh or gC…
A patch-based architecture for multi-label classification from single label annotations
Warren Jouanneau, Aurélie Bugeau, Marc Palyart +2
In this paper, we propose a patch-based architecture for multi-label classification problems where only a single positive label is observed in images of the dataset. Our contributi…
Analysis of Different Losses for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Image colorization aims to add color information to a grayscale image in a realistic way. Recent methods mostly rely on deep learning strategies. While learning to automatically co…
An Analysis of Generative Methods for Multiple Image Inpainting
Coloma Ballester, Aurelie Bugeau, Samuel Hurault +2
Image inpainting refers to the restoration of an image with missing regions in a way that is not detectable by the observer. The inpainting regions can be of any size and shape. Th…
Influence of Color Spaces for Deep Learning Image Colorization
Coloma Ballester, Aurélie Bugeau, Hernan Carrillo +4
Colorization is a process that converts a grayscale image into a color one that looks as natural as possible. Over the years this task has received a lot of attention. Existing col…
Unraveling the Hidden Environmental Impacts of AI Solutions for Environment
Anne-Laure Ligozat, Julien Lefèvre, Aurélie Bugeau +1
In the past ten years, artificial intelligence has encountered such dramatic progress that it is now seen as a tool of choice to solve environmental issues and in the first place g…
3D Object Detection and Pose Estimation of Unseen Objects in Color Images with Local Surface Embeddings
Giorgia Pitteri, Aurélie Bugeau, Slobodan Ilic +1
We present an approach for detecting and estimating the 3D poses of objects in images that requires only an untextured CAD model and no training phase for new objects. Our approach…
Multi-task deep learning for image segmentation using recursive approximation tasks
Rihuan Ke, Aurélie Bugeau, Nicolas Papadakis +3
Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop a mul…
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…
LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
Pierre Biasutti, Vincent Lepetit, Jean-François Aujol +2
We propose LU-Net -- for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet,…
RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
Pierre Biasutti, Aurélie Bugeau, Jean-François Aujol +1
This paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cl…
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…
SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches
Rémi Giraud, Vinh-Thong Ta, Aurélie Bugeau +2
Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non…
Multi-Layer Local Graph Words for Object Recognition
Svebor Karaman, Jenny Benois-Pineau, Rémi Mégret +1
In this paper, we propose a new multi-layer structural approach for the task of object based image retrieval. In our work we tackle the problem of structural organization of local…
Bandwidth selection for kernel estimation in mixed multi-dimensional spaces
Aurelie Bugeau, Patrick Pérez
Kernel estimation techniques, such as mean shift, suffer from one major drawback: the kernel bandwidth selection. The bandwidth can be fixed for all the data set or can vary at eac…