Clavier virtuel

Résumé

thumb|Un clavier virtuel sur un Pocket PC. Un clavier virtuel (également appelé clavier visuel dans l'environnement Microsoft Windows) est un logiciel permettant à l'utilisateur de saisir des caractères. Il peut être utilisé avec plusieurs dispositifs de pointage, comme une souris, ou un écran tactile. Il se substitue au clavier physique. Les claviers virtuels sont notamment employés pour rendre un système informatique accessible, ou pour taper du texte dans un alphabet autre que celui du clavier physique. Accessibilité aux personnes handicapées Un clavier virtuel peut être utilisé afin de rendre accessible un ordinateur à des personnes en situation de handicap moteur. Certains claviers virtuels ont été créés spécifiquement pour cela et permettent d'utiliser un ordinateur dans sa totalité en ne faisant usage que d'un contacteur. Sécurité contre les keylogger Si un ordinateur est susceptible d’être espionné par un enregistreur de frappe, l’utilisation d’un clav

Source officielle

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

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Concepts associés (21)

Une tablette tactile, tablette électronique, tablette numérique, ou tout simplement tablette, est un assistant personnel ou un ordinateur portable ultraplat qui se présente sous la forme d'un écran

Un clavier d’ordinateur est une interface homme-machine munie de touches permettant à l'utilisateur d'entrer dans l'ordinateur une séquence de données, notamment textuelle. Les touches sont général

Un smartphone ou téléphone intelligent (au Québec et au Canada francophone, également appelé téléphone multifonction, mobile multifonction ou encore ordiphone) est

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This course gives an introduction to the fundamental concepts and methods of the Digital Humanities, both from a theoretical and applied point of view. The course introduces the Digital Humanities circle of processing and interpretation, from data acquisition to new understandings.

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Application of 3D range camera in virtual human-computer interfaces

Huan Du

In this work, we propose new ways to employ 3D-ranging systems in advanced human-computer interfaces and show that they can be used for precise hand-tracking systems aiming at virtual keyboard or mouse applications. We first implement a Structured Light (SL) based 3D-ranging system as the testbed for acquiring depth information. The ranging system uses an off-the-shelf projector combined with an industrial color camera, where the projector projects well-designed line patterns onto the scene and the camera captures the reflected light from the scene and the target. The depth information is recovered from the deformed line pattern and the pre-calibrated projector-camera system. With this system we can capture dense depth maps with moderate resolution and high frame rate as the simulated input to the hand-tracking system. We then explore an approach to 3D hand matching that combines a detailed 3D hand model (skeletal + polygonal model) with a Physical-based Model Fitting technique (PMF). Virtual forces are assigned between the depth measurements and the polygonal vertices of the model. The matching problem is formulated as the minimization of the distances between the measurement and the model surface with the guidance of virtual forces. This can employ 3D information simply and directly onto the parameter space, and yield good results under challenging conditions. To demonstrate the effectiveness of the approach we show that we can track hand motions such as typing or grasping frame by frame. The major limitation of this method is that it requires a good initialization of the hand model and the computation time to achieve convergence with good fitting results is quite long. We therefore investigated more complex hand models and feature-based matching techniques. A three-level hand model (skeletal + primitive + polygonal model) is proposed for the Multi-Level Feature Matching approach (MLFM). The matching is carried out from the bottom to top levels of different hand presentations, and distinctive features for each level extracted from both the measurements and the model are matched with Scaled Conjugate Gradient method (SCG). The matching result from lower level is used as the initial state of the model parameters for higher level matching. This hierarchical structure speeds up the optimization process and enhances the precision of the matching result. We demonstrate our approach with the same hand motion as in the previous method and compare the performance of these two methods. We show that the MLFM method is faster and more robust to hand occlusions than the PMF method. Finally, we present a more powerful approach based on the Back-Constrained Gaussian Process Latent Variable Model (BC-GPLVM) that import prior information of categorized hand motion such as typing. BC-GPLVM provides a low-dimensional embedding of hand motion data, with a density function that gives higher probability to gestures and motions close to normal typing motion. This model is learned from a small set of clusters of vast captured motion data. The prior model is then combined with the previous multi-level models and features to generate a uniform cost function for optimization. We show the tracking result to demonstrate that this method can be used for human-computer interfaces requiring precise 3D hand tracking.

EPFL2008

Chargement

A Virtual Keyboard Based on True-3D Optical Ranging

Edoardo Charbon, Huan Du, Félix Lustenberger

In this paper, a complete system is presented which mimics a QWERTY keyboard on an arbitrary surface. The system consists of a pattern projector and a true-3D range camera for detecting the typing events. We exploit depth information acquired with the 3D range camera and detect the hand region using a pre-computed reference frame. The fingertips are found by analyzing the hands' contour and fitting the depth curve with different feature models. To detect a keystroke, we analyze the feature of the depth curve and map it back to a global coordinate system to find which key was pressed. These steps are fully automated and do not require human intervention. The system can be used in any application requiring zero form factor and minimized or no contact with a medium, as in a large number of cases in human-to-computer interaction, virtual reality, game control, 3D designs, etc.

2005

Chargement

In this paper a Multi-Level Feature Matching (MLFM) method is presented for 3D hand posture reconstruction of a virtual keyboard system. The human hand is modeled with a mixture of different levels of detail, from skeletal to polygonal surface representation. Different types of features are extracted and paired with the corresponding model. The matching is performed in a bottom-up order by SCG optimization with respect to the state vector of motion parameters. The low level of matching provide initial guess to the high level of matching, refining the precise position of the hand hierarchically. The matching results show that this method is effective for tracking human hand typing motion, even with noisy 3D depth map reconstruction and roughly detected fingertips. Examples of applications include virtual reality, gaming, 3D design, etc.

2008

Chargement

Application of 3D range camera in virtual human-computer interfaces

Huan Du

EPFL2008