Interface graphique

Résumé

thumb|Quelques widgets typiques. En informatique, une interface graphique (en anglais GUI pour graphical user interface) ou un environnement graphique est un dispositif de dialogue homme-machine, dans lequel les objets à manipuler sont dessinés sous forme de pictogrammes à l'écran, de sorte que l'usager peut les utiliser en imitant la manipulation physique de ces objets avec un dispositif de pointage, le plus souvent une souris. Ce type d'interface a été créé en 1973 sur le Xerox Alto par les ingénieurs du Xerox PARC pour remplacer les interfaces en ligne de commande. Mis sur le marché à la fin des années 1970 avec le Star de Xerox, le Lisa d'Apple est popularisé par cette dernière firme avec l'ordinateur Macintosh, commercialisé en 1984. Les interfaces graphiques sont mises en œuvre par un ensemble de logiciels souvent inclus dans les systèmes d'exploitation (Windows) ou fournis avec eux par les distributions (Linux). Ils sont devenus vers le milieu des années 1990 le standard des a

Source officielle

https://fr.wikipedia.org/wiki/Interface_graphique

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (100)

Publications associées

Chargement

Personnes associées (24)

Personnes associées

Chargement

Unités associées (20)

Unités associées

Chargement

Concepts associés (136)

Concepts associés

Chargement

Cours associés (86)

Cours associés

Chargement

Séances de cours associées (161)

Séances de cours associées

Chargement

, , ,

Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open-source toolbox called DeepLabCut that builds on a state-of-the-art human pose-estimation algorithm to allow a user to train a deep neural network with limited training data to precisely track user-defined features that match human labeling accuracy. Here, we provide an updated toolbox, developed as a Python package, that includes new features such as graphical user interfaces (GUIs), performance improvements, and active-learning-based network refinement. We provide a step-by-step procedure for using DeepLabCut that guides the user in creating a tailored, reusable analysis pipeline with a graphical processing unit (GPU) in 1–12 h (depending on frame size). Additionally, we provide Docker environments and Jupyter Notebooks that can be run on cloud resources such as Google Colaboratory.

2019

Chargement

, , ,

Noninvasive behavioral tracking of animals during experiments is crucial to many scientific pursuits. Extracting the poses of animals without using markers is often essential for measuring behavioral effects in biomechanics, genetics, ethology & neuroscience. Yet, extracting detailed poses without markers in dynamically changing backgrounds has been challenging. We recently introduced an open source toolbox called DeepLabCut that builds on a state-of-the-art human pose estimation algorithm to allow a user to train a deep neural network using limited training data to precisely track user-defined features that matches human labeling accuracy. Here, with this paper we provide an updated toolbox that is self contained within a Python package that includes new features such as graphical user interfaces and active-learning based network refinement. Lastly, we provide a step-by-step guide for using DeepLabCut.

Neuroscience2018

Chargement

New horizons for patient safety: LIGRA (LIfe Guard for Robotic surgery Assistance) An interactive platform centralizing information and control in robotic surgery Operating Rooms

Hannes Bleuler

The present paper describes LIGRA, an innovative technological solution developed in the context of the SAFROS co-funded EU project. Its general goal is to improve patient safety during minimal invasive robotic surgery. Focusing on the risks introduced in the surgical workflow by new robotic technologies, the LIGRA project consists in the design and implementation of a graphical user interface (GUI), on top of a dedicated middleware for message transport. This GUI is intuitive, non-invasive and versatile. Being visible to the whole surgical staff and easily understandable, the interface monitors the status of all robotic components, informs about any potential dangerous situation and displays the needed steps to recover. In addition, LIGRA records the data sent by the components, allowing retrieving technical and procedural information about the surgery and the staff afterwards. Furthermore, it gives access to an interactive version of the World Health Organization Patient Safety Checklist that recalls the essential checks and verifications to be made prior, during, and after the surgery. The approach adopted during this contribution, justified by the lack of a similar tool in OR nowadays, is to establish a common framework, which could have an important positive impact on patient safety.

Ieee2013

Chargement