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

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

Unsupervised learning-based approach for detecting 3D edges in depth maps

3D edge features, which represent the boundaries between different objects or surfaces in a 3D scene, are crucial for many computer vision tasks, including object recognition, tracking, and segmentation. They also have numerous real-world applications in the field of robotics, such as vision-guided grasping and manipulation of objects. To extract these features in the noisy real-world depth data, reliable 3D edge detectors are indispensable. However, currently available 3D edge detection methods are either highly parameterized or require ground truth labelling, which makes them challenging to use for practical applications. To this extent, we present a new 3D edge detection approach using unsupervised classification. Our method learns features from depth maps at three different scales using an encoder-decoder network, from which edge-specific features are extracted. These edge features are then clustered using learning to classify each point as an edge or not. The proposed method has two key benefits. First, it eliminates the need for manual fine-tuning of data-specific hyper-parameters and automatically selects threshold values for edge classification. Second, the method does not require any labelled training data, unlike many state-of-the-art methods that require supervised training with extensive hand-labelled datasets. The proposed method is evaluated on five benchmark datasets with single and multi-object scenes, and compared with four state-of-the-art edge detection methods from the literature. Results demonstrate that the proposed method achieves competitive performance, despite not using any labelled data or relying on hand-tuning of key parameters.</p

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

Visual Servoing Schemes for Automatic Nanopositioning Under Scanning Electron Microscope.

International audienceThis paper presents two visual servoing approaches for nanopositioning in a scanning electron microscope (SEM). The first approach uses the total pixel intensities of an image as visual measurements for designing the control law. The positioning error and the platform control are directly linked with the intensity variations. The second approach is a frequency domain method that uses Fourier transform to compute the relative motion between images. In this case, the control law is designed to minimize the error i.e. the 2D motion between current and desired images by controlling the positioning platform movement. Both methods are validated at different experimental conditions for a task of positioning silicon microparts using a piezo-positioning platform. The obtained results demonstrate the efficiency and robustness of the developed methods

Haptic-guided assisted telemanipulation approach for grasping desired objects from heaps

This paper presents an assisted telemanipulation framework for reaching and grasping desired objects from clutter. Specifically, the developed system allows an operator to select an object from a cluttered heap and effortlessly grasp it, with the system assisting in selecting the best grasp and guiding the operator to reach it. To this end, we propose an object pose estimation scheme, a dynamic grasp re-ranking strategy, and a reach-to-grasp hybrid force/position trajectory guidance controller. We integrate them, along with our previous SpectGRASP grasp planner, into a classical bilateral teleoperation system that allows to control the robot using a haptic device while providing force feedback to the operator. For a user-selected object, our system first identifies the object in the heap and estimates its full six degrees of freedom (DoF) pose. Then, SpectGRASP generates a set of ordered, collision-free grasps for this object. Based on the current location of the robot gripper, the proposed grasp re-ranking strategy dynamically updates the best grasp. In assisted mode, the hybrid controller generates a zero force-torque path along the reach-to-grasp trajectory while automatically controlling the orientation of the robot. We conducted real-world experiments using a haptic device and a 7-DoF cobot with a 2-finger gripper to validate individual components of our telemanipulation system and its overall functionality. Obtained results demonstrate the effectiveness of our system in assisting humans to clear cluttered scenes.Comment: Accepted to 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC

Visual Servoing-Based approach for efficient autofocusing in Scanning Electron Microscope.

International audienceFast and reliable autofocusing methods are essential for performing automatic nano-objects positioning tasks using a scanning electron microscope (SEM). So far in the literature, various autofocusing algorithms have been proposed utilizing a sharpness measure to compute the best focus. Most of them are based on iterative search approaches; applying the sharpness function over the total range of focus to find an image in-focus. In this paper, a new, fast and direct method of autofocusing has been presented based on the idea of traditional visual servoing to control the focus step using an adaptive gain. The visual control law is validated using a normalized variance sharpness function. The obtained experimental results demonstrate the performance of the proposed autofocusing method in terms of accuracy, speed and robustness

Gluing free assembly of an advanced 3D structure using visual servoing.

International audienceThe paper deals with robotic assembly of 5 parts by their U-grooves to achieve stables 3D MEMS, without any use of soldering effect. The parts and their grooves measure 400 m 400 m 100 m 1.5 m and 100 m 100 m 100 m 1.5 m leading to an assembly clearance ranging from -3 and +3 m. Two visual servo approaches are used simultaneously: 2D visual servo for gripping and release of parts and 3D visual servo for displacement of parts. The results of experiments are presented and analyzed