Depth and Shape Estimation from Focus in Scanning Electron Microscope for Micromanipulation.

International audienceInter-object depth estimation is always a major concern for micromanipulation using scanning electron microscope (SEM). So far, various methods have been proposed for estimating this depth based on stereoscopic imaging. Most of them require external hardware unit or manual interaction during the process. In this paper, using the image focus information, different methods are presented for estimating the inter-object depth for micromanipulation and the local pixel point depth for 3D shape reconstruction. In both cases, the normalized variance has been used as the sharpness criteria. For interobject depth estimation, a visual servoing-based autofocusing method has been used to maximize the sharpness in object region windows. For Shape reconstruction, a stack of images are acquired by varying the working distance. These images are processed to find the maximum sharpness of each pixel and consequently reconstructing the surface. Developments are validated in a robotic handling scenario where the scene contains a microgripper and silicon microstructures

Performance Evaluation of Scanning Electron Microscopes using Signal-to-Noise Ratio.

International audienceScanning Electron Microscope is becoming a vital imaging tool in desktop laboratories because of its high imaging capability. Through this work we evaluate the performance of two different SEMs consisting of a tungsten gun and a field effect gun, with respect to time and magnification by estimating their image signalto- noise ratio. SNR is mainly applied to quantify the level of image noise over changes in the acquisition time and magnification rates. Majority of the existing methods to estimate this quantity are based on crosscorrelation technique and requires two images of the same specimen area. In this paper we propose a simple and efficient technique to compute signal-to-noise ratio using median filters. Unlike other techniques the proposed method uses only a single image and can be used in real time applications. The derived results show the effectiveness of the developed algorithm

Scanning electron microscope image signal-to-noise ratio monitoring for micro-nanomanipulation.

International audienceAs an imaging system, scanning electron microscope (SEM) performs an important role in autonomous micro-nanomanipulation applications. When it comes to the sub micrometer range and at high scanning speeds, the images produced by the SEM are noisy and need to be evaluated or corrected beforehand. In this article, the quality of images produced by a tungsten gun SEM has been evaluated by quantifying the level of image signal-to-noise ratio (SNR). In order to determine the SNR, an efficient and online monitoring method is developed based on the nonlinear filtering using a single image. Using this method, the quality of images produced by a tungsten gun SEM is monitored at different experimental conditions. The derived results demonstrate the developed method's efficiency in SNR quantification and illustrate the imaging quality evolution in SEM

Fast Image Drift Compensation in Scanning Electron Microscope using Image Registration.

International audienceScanning Electron Microscope (SEM) image acquisition is mostly affected by the time varying motion of pixel positions in the consecutive images, a phenomenon called drift. In order to perform accurate measurements using SEM, it is necessary to compensate this drift in advance. Most of the existing drift compensation methods were developed using the image correlation technique. In this paper, we present an image registration-based drift compensation method, where the correction on the distorted image is performed by computing the homography, using the keypoint correspondences between the images. Four keypoint detection algorithms have been used for this work. The obtained experimental results demonstrate the method's performance and efficiency in comparison with the correlation technique

Synthesizing a virtual imager with a large field of view and a high resolution for micromanipulation.

International audiencePhoton microscope connected with a camera is the usual imager required in micromanipulation applications. That microimager gives high resolution views, but the corresponding field of view are very narrow and do not allow the vision of the entire workfield. The classical solution consists in using multiple views imaging system: a high resolution imager for local view and a low resolution imager for global view. We are developing an alternative solution based on image mosaicing that requires only one microimager. The views from that real microimager are associated in order to achieve a virtual microimager which combines a large field of view with a high resolution

Toward the vision based supervision of microfactories through images mosaicing.

International audienceThe microfactory paradigm means the miniaturisation of manufacturing systems according to the miniaturisation of products. Some benefits are the saving of material, energy and place. A vision based solution to the problem of supervision of microfactories is proposed. It consists in synthetising a high resolution global view of the work field and real time inlay of local image in this background. The result can be used for micromanipulation monitoring, assistance to the operator, alarms and others useful informations displaying

Virtual camera synthesis for Micromanipulation and Microassembly.

International audienceThe vision system for micromanipulation and microassembly usually includes at least two cameras allowing top and lateral views of the work field. The top view is used to control the xy position of the microgripper. As for the lateral view, it allows the control of the z position. The paper describes how the lateral camera can be replaced by a virtual one using the trifocal transfer. Then, the work field is set free. The novel view synthesis method used requires only a weak calibration to recover the geometry of the scene, then, it is well suited for the vision at the microscale

Hysteretic Q-Learning : an algorithm for decentralized reinforcement learning in cooperative multi-agent teams.

International audienceMulti-agent systems (MAS) are a field of study of growing interest in a variety of domains such as robotics or distributed controls. The article focuses on decentralized reinforcement learning (RL) in cooperative MAS, where a team of independent learning robot (IL) try to coordinate their individual behavior to reach a coherent joint behavior. We assume that each robot has no information about its teammates'actions. To date, RL approaches for such ILs did not guarantee convergence to the optimal joint policy in scenarios where the coordination is difficult. We report an investigation of existing algorithms for the learning of coordination in cooperative MAS, and suggest a Q-Learning extension for ILs, called Hysteretic Q-Learning. This algorithm does not require any additional communication between robots. Its advantages are showing off and compared to other methods on various applications : bimatrix games, collaborative ball balancing task and pursuit domain

Choix de la fonction de renforcement et des valeurs initiales pour accélérer les problèmes d'Apprentissage par Renforcement de plus court chemin stochastique.

National audienceUn point important en apprentissage par renforcement (AR) est l'amélioration de la vitesse de convergence du processus d'apprentissage. Nous proposons dans cet article d'étudier l'influence de certains paramètres de l'AR sur la vitesse d'apprentissage. En effet, bien que les propriétés de convergence de l'AR ont été largement étudiées, peu de règles précises existent pour choisir correctement la fonction de renforcement et les valeurs initiales de la table Q. Notre méthode aide au choix de ces paramètres dans le cadre de problèmes de type goal-directed, c'est-à-dire dont l'objectif est d'atteindre un but en un minimum de temps. Nous développons une étude théorique et proposons ensuite des justifications expérimentales pour choisir d'une part la fonction de renforcement et d'autre part des valeurs initiales particulières de la table Q, basées sur une fonction d'influence

Reward function and initial values : Better choices for accelerated Goal-directed reinforcement learning.

International audienceAn important issue in Reinforcement Learning (RL) is to accelerate or improve the learning process. In this paper, we study the influence of some RL parameters over the learning speed. Indeed, although RL convergence properties have been widely studied, no precise rules exist to correctly choose the reward function and initial Q-values. Our method helps the choice of these RL parameters within the context of reaching a goal in a minimal time. We develop a theoretical study and also provide experimental justifications for choosing on the one hand the reward function, and on the other hand particular initial Q-values based on a goal bias function