Visual Servoing-Based Depth-Estimation Technique for Manipulation Inside SEM

Marturi, Naresh; Tamadazte, Brahim; Dembélé, Sounkalo; Piat, Nadine

doi:10.1109/tim.2016.2556898

Cited by 18 publications

(11 citation statements)

References 17 publications

(17 reference statements)

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“…Unfortunately, their method was not feasible for our system, as we could not afford to capture and analyze seven images before actually finding the peak. Similar to our proposal, Marturi, Tamadazte, Dembélé, and Piat [9] had the necessity of a moving, focused target. They managed this by moving and focusing serially.…”

Section: Related Worksupporting

confidence: 74%

Validating Autofocus Algorithms with Automated Tests

Werner

Carrasco²

2018

Robotics

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Abstract:For an automated camera focus, a fast and reliable algorithm is key to its success. It should work in a precisely defined way for as many cases as possible. However, there are many parameters which have to be fine-tuned for it to work exactly as intended. Most literature only focuses on the algorithm itself and tests it with simulations or renderings, but not in real settings. Trying to gather this data by manually placing objects in front of the camera is not feasible, as no human can perform one movement repeatedly in the same way, which makes an objective comparison impossible. We therefore used a small industrial robot with a set of over 250 combinations of movement, pattern, and zoom-states to conduct these tests. The benefit of this method was the objectivity of the data and the monitoring of the important thresholds. Our interest laid in the optimization of an existing algorithm, by showing its performance in as many benchmarks as possible. This included standard use cases and worst-case scenarios. To validate our method, we gathered data from a first run, adapted the algorithm, and conducted the tests again. The second run showed improved performance.

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Section: Related Worksupporting

confidence: 74%

Validating Autofocus Algorithms with Automated Tests

Werner

Carrasco²

2018

Robotics

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“…The signals that derive from electron-sample interactions reveal information about the sample including external morphology (texture), chemical composition, and crystalline structure and orientation of materials making up the sample. Areas ranging from approximately 1 cm to 5 microns in width can be imaged in a scanning mode using conventional SEM techniques (magnification ranging from 20X to approximately 300000X, spatial resolution from 50 to 100 nm) [11,12]. SEM devices have at least one detector.…”

Section: Sem Setup Descriptionmentioning

confidence: 99%

A New Procedure for Porous Material Characterization

Chilev¹

2017

IJSTS

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Abstract:A new procedure for quantitative characterization of different types of solid materials is proposed. The technique is based on the Scanning Electron Microscopy (SEM) analysis results of porous materials and their processing by the software ImageJ. Several types of porous adsorbents AX21, AC35, GAC250, ACENO and IRH3 activated carbons were investigated. Based on SEM analysis, different characteristics of the samples such as porosity, pore size distribution, bed particles porosity can be obtained. In this study, the particle size, the average macropore size and pore size distributions (PSD) of samples were determined with a new procedure for SEM analysis treatment using ImageJ software. Three distribution functions (Gamma, Weibull and Lognormal) were selected to describe the experimental results. The Lognormal distribution fitted more accurately the experimental data.

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“…The 3D images were generated by tilting an electron beam, followed by a stereo algorithm based on a biologically motivated energy model [ 21 ]. Otherwise, objects positioned in the Z-direction can be estimated by image sharpness [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM

Ding

Shi

et al. 2018

Sensors

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The maneuvering and electrical characterization of nanotubes inside a scanning electron microscope (SEM) has historically been time-consuming and laborious for operators. Before the development of automated nanomanipulation-enabled techniques for the performance of pick-and-place and characterization of nanoobjects, these functions were still incomplete and largely operated manually. In this paper, a dual-probe nanomanipulation system vision-based feedback was demonstrated to automatically perform 3D nanomanipulation tasks, to investigate the electrical characterization of nanotubes. The XY-position of Atomic Force Microscope (AFM) cantilevers and individual carbon nanotubes (CNTs) were precisely recognized via a series of image processing operations. A coarse-to-fine positioning strategy in the Z-direction was applied through the combination of the sharpness-based depth estimation method and the contact-detection method. The use of nanorobotic magnification-regulated speed aided in improving working efficiency and reliability. Additionally, we proposed automated alignment of manipulator axes by visual tracking the movement trajectory of the end effector. The experimental results indicate the system’s capability for automated measurement electrical characterization of CNTs. Furthermore, the automated nanomanipulation system has the potential to be extended to other nanomanipulation tasks.

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Visual Servoing-Based Depth-Estimation Technique for Manipulation Inside SEM

Cited by 18 publications

References 17 publications

Validating Autofocus Algorithms with Automated Tests

Validating Autofocus Algorithms with Automated Tests

A New Procedure for Porous Material Characterization

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM

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