La réalité augmentée bouleverse de nombreux secteurs industriels en offrant une nouvelle perception de l’environnement.
Par son expertise en vision par ordinateur, estimation de pose et visualisation 3D, le laboratoire Technologies Immersives & Médicales conçoit composants et plateformes pour augmenter les expériences des professionnels et renforcer leur efficacité, notamment grâce à la réalité augmentée et la réalité virtuelle, dans des domaines tels que l’industrie et la santé. L’utilisateur est au cœur de sa réflexion et de ses travaux, depuis la définition des besoins jusqu’à la confrontation à l’usage des technologies développées. Dans le domaine de la santé, le laboratoire travaille, avec ses partenaires médicaux, sur des technologies clefs telles que le traitement des images et des vidéos médicales, la connectivité ou l’interopérabilité.
13.03.2020
Camera relocalization is an important component in localization systems such as augmented reality or robotics when camera tracking loss occurs. It uses models built from known information of a scene. However, these models cannot perform in a dynamic environment which contains moving objects. In this paper, we propose an adaptive regression forest and apply it to our DynaLoc, a real-time camera…
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26.01.2021
TOM: a self-trained Tomography solution for Overlay networks Monitoring
Network tomography is a discipline that aims to infer the internal network characteristics from end-to-end correlated measurements performed at the network edge. This work presents a new tomography approach for link metrics inference in an SDN/NFV environment (even if it can be exported outside this field) that we called TOM (Tomography for Overlay networks Monitoring). In such an environment, we…
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14.11.2019
Camera relocalization refers to the problematic of the camera pose estimation including 3D translation and 3D rotation expressed in the world coordinate system with no temporal constraint. Camera relocalization is necessary in localization systems. However, it is still challenging to have both a real-time and accurate method. In this paper, we introduce our data-oriented hybrid method merging…
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04.03.2020
Assisted phase and step annotation for surgical videos
Purpose - Annotation of surgical videos is a time-consuming task which requires specific knowledge. In this paper, we present and evaluate a deep learning-based method that includes pre-annotation of the phases and steps in surgical videos and user assistance in the annotation process. Methods - We propose a classification function that automatically detects errors and infers temporal coherence…
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