150 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de BourgogneFR13 papers
- Centre National de la Recherche ScientifiqueFR7 papers
- Vision pour la RobotiqueFR7 papers
- Laboratoire Interdisciplinaire Carnot de BourgogneFR4 papers
- CHU Dijon BourgogneFR2 papers
- Institut CurieFR2 papers
- Instituto de Investigación en Ingeniería de AragónES2 papers
- Laboratoire d'Informatique, du Traitement de l'Information et des SystèmesFR2 papers
- Maison des Sciences sociales et des Humanités de DijonFR2 papers
- Université Bourgogne Franche-ComtéFR2 papers
- Alstom (France)FR1 paper
- Business FranceFR1 paper
17 papers
Leveraging Foundation Models To learn the shape of semi-fluid deformable objects
Omar El Assal, Carlos M. Mateo, Sebastien Ciron +1
One of the difficulties imposed on the manipulation of deformable objects is their characterization and the detection of representative keypoints for the purpose of manipulation. A…
Steering Prediction via a Multi-Sensor System for Autonomous Racing
Zhuyun Zhou, Zongwei Wu, Florian Bolli +5
Autonomous racing has rapidly gained research attention. Traditionally, racing cars rely on 2D LiDAR as their primary visual system. In this work, we explore the integration of an…
Pola4All: survey of polarimetric applications and an open-source toolkit to analyze polarization
Joaquin Rodriguez, Lew-Fock-Chong Lew-Yan-Voon, Renato Martins +1
Polarization information of the light can provide rich cues for computer vision and scene understanding tasks, such as the type of material, pose, and shape of the objects. With th…
Event-Free Moving Object Segmentation from Moving Ego Vehicle
Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel +5
Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving, especially for sequences obtained from mo…
HiDAnet: RGB-D Salient Object Detection via Hierarchical Depth Awareness
Zongwei Wu, Guillaume Allibert, Fabrice Meriaudeau +2
RGB-D saliency detection aims to fuse multi-modal cues to accurately localize salient regions. Existing works often adopt attention modules for feature modeling, with few methods e…
Learning Geodesic-Aware Local Features from RGB-D Images
Guilherme Potje, Renato Martins, Felipe Cadar +1
Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfa…
Multi-Grid Redundant Bounding Box Annotation for Accurate Object Detection
Solomon Negussie Tesema, El-Bay Bourennane
Modern leading object detectors are either two-stage or one-stage networks repurposed from a deep CNN-based backbone classifier network. YOLOv3 is one such very-well known state-of…
Modality-Guided Subnetwork for Salient Object Detection
Zongwei Wu, Guillaume Allibert, Christophe Stolz +2
Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the…
Learning With Context Feedback Loop for Robust Medical Image Segmentation
Kibrom Berihu Girum, Gilles Créhange, Alain Lalande
Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixe…
ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation
Yu Liu, Fan Yang, Dominique Ginhac
Interpreting human actions requires understanding the spatial and temporal context of the scenes. State-of-the-art action detectors based on Convolutional Neural Network (CNN) have…
A Mutual Reference Shape for Segmentation Fusion and Evaluation
S. Jehan-Besson, R. Clouard, C. Tilmant +7
This paper proposes the estimation of a mutual shape from a set of different segmentation results using both active contours and information theory. The mutual shape is here define…
Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks
Kibrom Berihu Girum, Youssef Skandarani, Raabid Hussain +3
In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The propos…
Depth-Adapted CNN for RGB-D cameras
Zongwei Wu, Guillaume Allibert, Christophe Stolz +1
Conventional 2D Convolutional Neural Networks (CNN) extract features from an input image by applying linear filters. These filters compute the spatial coherence by weighting the ph…
Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning
Axel Aguerreberry, Ezequiel de la Rosa, Alain Lalande +1
Accurate and reproducible measurements of the aortic diameters are crucial for the diagnosis of cardiovascular diseases and for therapeutic decision making. Currently, these measur…
What's in my Room? Object Recognition on Indoor Panoramic Images
Julia Guerrero-Viu, Clara Fernandez-Labrador, Cédric Demonceaux +1
In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply. While several task…
Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks
Ezequiel de la Rosa, Désiré Sidibé, Thomas Decourselle +3
Significance: Late gadolinium enhanced magnetic resonance imaging (LGE-MRI) is the gold standard technique for myocardial viability assessment. Although the technique accurately re…
PanoRoom: From the Sphere to the 3D Layout
Clara Fernandez-Labrador, Jose M. Facil, Alejandro Perez-Yus +2
We propose a novel FCN able to work with omnidirectional images that outputs accurate probability maps representing the main structure of indoor scenes, which is able to generalize…