scholarly journals Comparison of Colored Particle Images Generated by Various Illumination Methods

2009 ◽  
Vol 29-1 (2) ◽  
pp. 1157-1157
Author(s):  
Toshinori Yamauchi ◽  
Yasufumi Yamamoto ◽  
Manabu Iguchi ◽  
Tomomasa Uemura
Keyword(s):  
Colored Particle ◽  
Particle Images

Characteristic Geometry and Diffraction Contrast of Dispersed ThO2 Crystals

1969 ◽  
Vol 27 ◽  
pp. 168-169
Author(s):  
R. J. Horylev ◽  
L. E. Murr
Keyword(s):  
Dark Field ◽  
Particle Orientation ◽  
Contrast Effects ◽  
Strain Fields ◽  
Diffraction Contrast ◽  
Dispersed Particles ◽  
Field Electron Microscopy ◽  
Dark Field Electron ◽  
Particle Images ◽  
Diffraction Effects

Smith has shown by dark-field electron microscopy of extracted ThO2 particles from TD-nickel (2% ThO2) that they possess single crystal characteristics. It is generally assumed that these particle dispersions are incoherent. However, some diffraction effects associated with the particle images appeared to be similar to coherency strain fields. The present work will demonstrate conclusively that ThO2 dispersed particles in TD-nickel (2% ThO2) and TD-NiCr (2% ThO2, 20% Cr, Ni) are single crystals. Moreover, the diffraction contrast effects are extinction fringes. That is, these effects arise because of the particle orientation with respect to the electron beam and the extinction conditions for various operating reflections The particles are in fact incoherent.

New challenges to image processing posed by cryo-Electron microscopy of single particles

1991 ◽  
Vol 49 ◽  
pp. 422-423
Author(s):  
Joachim Frank
Keyword(s):  
Electron Microscopy ◽  
Phase Contrast ◽  
Particle Orientation ◽  
Single Particles ◽  
Contrast Transfer Function ◽  
Virtual Absence ◽  
Cryo Electron Microscopy ◽  
Spatial Frequencies ◽  
New Challenges ◽  
Particle Images

Compared with images of negatively stained single particle specimens, those obtained by cryo-electron microscopy have the following new features: (a) higher “signal” variability due to a higher variability of particle orientation; (b) reduced signal/noise ratio (S/N); (c) virtual absence of low-spatial-frequency information related to elastic scattering, due to the properties of the phase contrast transfer function (PCTF); and (d) reduced resolution due to the efforts of the microscopist to boost the PCTF at low spatial frequencies, in his attempt to obtain recognizable particle images.

PIV Measurements Within the Waves of Wavy and Wavy-Annular Horizontal Two-Phase Flow

2005 ◽  
Author(s):  
R. E. Foster ◽  
T. A. Shedd
Keyword(s):  
Particle Image ◽  
Image Processing Algorithm ◽  
Two Phase ◽  
Piv Measurements ◽  
Post Process ◽  
Image Velocimetry ◽  
Processed Form ◽  
Image Pairs ◽  
Particle Images ◽  
The Waves

A novel technique of microscopic Particle Image Velocimetry (PIV) is presented for two-phase annular, wavy-annular and stratified flow. Seeding of opaque particles in a water/dye flow allows the acquisition of instantaneous film velocity data in the film cross-section at the center of the tube in the form of digital image pairs. An image processing algorithm is also described that allows numerical velocities to be distilled from particle images by commercial PIV software. The approach yields promising results for stratified and wavy-annular flows, however highly bubbly flows remain difficult to image and post-process. Initial data images are presented in raw and processed form.

scholarly journals Research on recognition method of cloud precipitation particle shape based on BP neural network

2021 ◽  
Vol 336 ◽  
pp. 06011
Author(s):  
Haonan Dong ◽  
Ruili Jiao ◽  
Minsong Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Particle Shape ◽  
Northern China ◽  
Recognition Method ◽  
Cloud Particle ◽  
Particle Images ◽  
Bp Neural Network Model

In order to solve the problem that the shape of cloud particle images measured by airborne cloud imaging probe (CIP) cannot be automatically recognized, this paper proposes an automatic recognition method of cloud and precipitation particle shape based on BP neural network. This method mainly uses a set of geometric parameters which can better describe the shape characteristics of cloud precipitation particles. Based on the cloud precipitation particle images measured by CIP in the precipitation stratiform clouds in northern China, a particle shape data training set and a testing set were constructed to train and verify the effect of the selected BP neural network model. The selected BP neural network model can classify the cloud particle image into tiny, column, needle, dendrite, aggregate, graupel, sphere, hexagonal and irregular. Utilizing the field campaign data measured by CIP, the habit identified results by the improved Holroyd method and by the selected BP neural network model were compared, which shows that the accuracy of BP neural network method is better than that of improved Holroyd method.

Effects of particle locations on reconstructed particle images in digital holography

2016 ◽  
Vol 55 (33) ◽  
pp. 9532 ◽  
Author(s):  
Christina Hesseling ◽  
Tim Homeyer ◽  
Joachim Peinke ◽  
Gerd Gülker
Keyword(s):  
Digital Holography ◽  
Particle Images

Automated Detection of Paleoenvironmental Proxy, Eucampia Index, in a Microscopic Slide Using a Convolutional Neural Network System

2020 ◽  
Author(s):  
Saki Ishino ◽  
Takuya Itaki
Keyword(s):  
Southern Ocean ◽  
Large Scale ◽  
Classification Performance ◽  
Automated Detection ◽  
Model Verification ◽  
Training Dataset ◽  
Test Dataset ◽  
Counting Error ◽  
Index Value ◽  
Particle Images

Abstract The Eucampia Index, which is calculated from valve ratio of Antarctic diatom Eucampia ainarctica varieties, has been expected to be a useful indicator of sea ice coverage or/and sea surface temperature variation in the Southern Ocean. To verify the relationship between the index value and the environmental factors, considerable effort is needed to classify and count valves of E. antarctica in a very large number of samples. In this study, to realize automated detection of the Eucampia Index, we constructed a deep-learning (one of the learning methods of artificial intelligence) based models for identifying Eucampia valves from various particles in a diatom slide. The microfossil Classification and Rapid Accumulation Device (miCRAD) system, which can be used for scanning a slide and cropping images of particles automatically, was employed to collect images in training dataset for the model and test dataset for model verification. As a result of classifying particle images in the test dataset by the initial model "Eant_1000px_200616", accuracy was 78.8%. The Eucampia Index value prepared in the test dataset was 0.80, and the value predicted using the developed model from the same dataset was 0.76. The predicted value was in the range of the manual counting error. These results suggest that the classification performance of the model is similar to that of a human expert. This study revealed that a model capable of detecting the ratio of two diatom species can be constructed using the miCRAD system for the first time. The miCRAD system connected with the developed model in this study is capable of automatically classifying particle images at the same time of capturing images so that the system can be applied to a large-scale analysis of the Eucampia index in the Southern Ocean. Depending on the setting of the classification category, similar method is relevant to investigators who have to process a large number of diatom samples such as for detecting specific species for biostratigraphic and paleoenvironmental studies.

scholarly journals Deep-learning with synthetic data enables automated picking of cryo-EM particle images of biological macromolecules

2019 ◽  
Author(s):  
Ruijie Yao ◽  
Jiaqiang Qian ◽  
Qiang Huang
Keyword(s):  
Deep Learning ◽  
Single Particle ◽  
Feature Matching ◽  
Synthetic Data ◽  
Supplementary Information ◽  
Particle Analysis ◽  
Biological Macromolecules ◽  
Low Contrast ◽  
3D Structures ◽  
Particle Images

Abstract Motivation Single-particle cryo-electron microscopy (cryo-EM) has become a powerful technique for determining 3D structures of biological macromolecules at near-atomic resolution. However, this approach requires picking huge numbers of macromolecular particle images from thousands of low-contrast, high-noisy electron micrographs. Although machine-learning methods were developed to get rid of this bottleneck, it still lacks universal methods that could automatically picking the noisy cryo-EM particles of various macromolecules. Results Here, we present a deep-learning segmentation model that employs fully convolutional networks trained with synthetic data of known 3D structures, called PARSED (PARticle SEgmentation Detector). Without using any experimental information, PARSED could automatically segment the cryo-EM particles in a whole micrograph at a time, enabling faster particle picking than previous template/feature-matching and particle-classification methods. Applications to six large public cryo-EM datasets clearly validated its universal ability to pick macromolecular particles of various sizes. Thus, our deep-learning method could break the particle-picking bottleneck in the single-particle analysis, and thereby accelerates the high-resolution structure determination by cryo-EM. Availability and implementation The PARSED package and user manual for noncommercial use are available as Supplementary Material (in the compressed file: parsed_v1.zip). Supplementary information Supplementary data are available at Bioinformatics online.

Development and Application of LED Illumination Color PIV

2006 ◽  
Author(s):  
Shenq-Yuh Jaw ◽  
Robert R. Hwang ◽  
K. L. Shyu
Keyword(s):  
Turbulent Flows ◽  
Channel Flow ◽  
Particle Image ◽  
Time Derivative ◽  
Digital Camera ◽  
Soap Film ◽  
Velocity Fields ◽  
Flow Measurements ◽  
Full Field ◽  
Particle Images

In this study, red, green, and blue light-emitting diodes (LED) are adopted as the light source to illuminate sequentially a two-dimensional soap film channel flow. Triple-exposure particle image is recorded on the same image frame by a 3-ccd color camera. Since the particles illuminated by the R, G, B LED will only be recorded on the R, G, B ccd-chip of the digital camera, three sequential exposure, R, G, B particle images can be obtained from separating the triple-exposure particle image. Two sequential velocity fields can be determined from the correlation analysis of the R-G and G-B sequential particle images. Time derivative of the velocity fields, and hence the evolution of the unsteady flow or the characteristics of turbulent flows can be analyzed from the two velocity fields determined. The color PIV method incorporated with the LED light has proven to be a cheap, safe, and powerful tool for the full-field flow measurements. Results of the flow past circular cylinder in the confined soap film channel flow are presented.

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