scholarly journals Automated detection and localization of synaptic vesicles in electron microscopy images

eNeuro ◽  
2022 ◽  
pp. ENEURO.0400-20.2021
Author(s):  
Barbara Imbrosci ◽  
Dietmar Schmitz ◽  
Marta Orlando
Keyword(s):  
Electron Microscopy ◽  
Synaptic Vesicles ◽  
Automated Detection ◽  
Detection And Localization ◽  
Microscopy Images

scholarly journals Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images

PLoS ONE ◽  
2011 ◽  
Vol 6 (10) ◽  
pp. e24899 ◽  
Author(s):  
Anna Kreshuk ◽  
Christoph N. Straehle ◽  
Christoph Sommer ◽  
Ullrich Koethe ◽  
Marco Cantoni ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Automated Detection ◽  
Synaptic Contacts ◽  
Serial Electron Microscopy ◽  
Microscopy Images

A Transmission Electron Microscopical Investigation of Phase Transformations in TiNi

1980 ◽  
Vol 38 ◽  
pp. 340-341
Author(s):  
P. Moine ◽  
G. M. Michal ◽  
R. Sinclair
Keyword(s):  
Electron Microscopy ◽  
Phase Transformations ◽  
Applied Stress ◽  
Structural Changes ◽  
Microscopical Investigation ◽  
Temperature Range ◽  
Transmission Electron ◽  
Electron Microscopical ◽  
Diffraction Effects ◽  
Microscopy Images

Premartensitic effects in near equiatomic TiNi have been pointed out by several authors(1-5). These include anomalous contrast in electron microscopy images (mottling, striations, etc. ),diffraction effects(diffuse streaks, extra reflections, etc.), a resistivity peak above Ms (temperature at which a perceptible amount of martensite is formed without applied stress). However the structural changes occuring in this temperature range are not well understood. The purpose of this study is to clarify these phenomena.

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

scholarly journals Corrosion Behavior and Mechanical Properties of a Nanocomposite Superhydrophobic Coating

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 652
Author(s):  
Divine Sebastian ◽  
Chun-Wei Yao ◽  
Lutfun Nipa ◽  
Ian Lian ◽  
Gary Twu
Keyword(s):  
Electron Microscopy ◽  
Mechanical Properties ◽  
Scanning Electron Microscopy ◽  
Atomic Force Microscopy ◽  
Nanocomposite Coating ◽  
Superhydrophobic Coating ◽  
Force Microscopy ◽  
Atomic Force ◽  
Microscopy Images ◽  
Scanning Electron

In this work, a mechanically durable anticorrosion superhydrophobic coating is developed using a nanocomposite coating solution composed of silica nanoparticles and epoxy resin. The nanocomposite coating developed was tested for its superhydrophobic behavior using goniometry; surface morphology using scanning electron microscopy and atomic force microscopy; elemental composition using energy dispersive X-ray spectroscopy; corrosion resistance using atomic force microscopy; and potentiodynamic polarization measurements. The nanocomposite coating possesses hierarchical micro/nanostructures, according to the scanning electron microscopy images, and the presence of such structures was further confirmed by the atomic force microscopy images. The developed nanocomposite coating was found to be highly superhydrophobic as well as corrosion resistant, according to the results from static contact angle measurement and potentiodynamic polarization measurement, respectively. The abrasion resistance and mechanical durability of the nanocomposite coating were studied by abrasion tests, and the mechanical properties such as reduced modulus and Berkovich hardness were evaluated with the aid of nanoindentation tests.

Deep Learning-based Discriminative Refocusing of Scanning Electron Microscopy Images for Materials Science

2021 ◽  
pp. 116987
Author(s):  
Juwon Na ◽  
Gyuwon Kim ◽  
Seong-Hoon Kang ◽  
Se-Jong Kim ◽  
Seungchul Lee
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Deep Learning ◽  
Materials Science ◽  
Microscopy Images ◽  
Scanning Electron
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