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Measuring the randomness of micro‐ and nanostructure spatial distributions: Effects of scanning electron microscope image processing and analysis.
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Journal of Microscopy. Jan2023, Vol. 289 Issue 1, p48-57. 10p.
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Mavrogonatos, A.;Papia, E‐M.;Dimitrakellis, P.;Constantoudis, V.
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Journal of Microscopy. Jan2023, Vol. 289 Issue 1, p48-57. 10p.
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Computer control of a scanning electron microscope for digital image processing of thermal-wave images [microform] / Percy Gilbert ... [and others].
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1987
Available at Available Merrill-Cazier Government Documents (Lower Level) (Call number: NAS 1.15:100157)
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| Location | Call No. | Status |
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| Merrill-Cazier Government Documents (Lower Level) | NAS 1.15:100157 | Available |
Material Compound-Property Retrieval Using Electron Microscope Images for Rubber Material Development
Yanagi, R.;Togo, R.;Maeda, K.;Ogawa, T.;Haseyama, M.
Reshma, S R;Beegum, T Rehannara
Conference
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2017 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) Signal Processing, Informatics, Communication and Energy Systems (SPICES), 2017 IEEE International Conference on. :1-7 Aug, 2017
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A Voronoi diagram approach for detecting defects in 3D printed fiber-reinforced polymers from microscope images
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Computational Visual Media. March, 2023, Vol. 9 Issue 1, p41, 16 p.
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Li, Xiang;McMains, Sara
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Computational Visual Media. March, 2023, Vol. 9 Issue 1, p41, 16 p.
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Surface coverage and size analysis of redispersed nanoparticles by image processing .
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Particulate Science & Technology. 2021, Vol. 39 Issue 4, p475-480. 6p.
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Sirisathitkul, Yaowarat;Sarmphim, Pharunee;Sirisathitkul, Chitnarong
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Particulate Science & Technology. 2021, Vol. 39 Issue 4, p475-480. 6p.
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Transmission Electron Microscope (TEM) image processing is implemented in the spatial ...
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Surface coverage and size analysis of redispersed nanoparticles by image processing .
Particulate Science & Technology. 2021, Vol. 39 Issue 4, p475-480. 6p.
Transmission Electron Microscope (TEM) image processing is implemented in the spatial distribution analysis of iron-platinum based nanoparticles, drop-casting on diethylene glycol (DEG). Before the redispersion in hexane with surfactant additions, these magnetic particles aggregate in the nanosuspension after a prolonged storage. The precise analysis of aggregations by conventional methods is not possible but the complication can be overcome in this work. Despite the aggregation, image processing with the Canny algorithm can separate the edges of overlapping nanoparticles resulting in comparable particle size determination before and after the redispersion. The distributions on DEG are compared in terms of the surface coverage. The nanosuspension without redispersion gives rise to the highest surface coverage around 0.28 since a large number of nanoparticles are either aggregated or in close contacts. Smaller numbers of nanoparticles from redispersed nanosuspensions are detected in each TEM image . Whereas the redispersed nanosuspension of 0.2 mg/mL still contains some nanoparticle stacking, the higher concentration of 0.3 mg/mL results in the lowest surface coverage of 0.18 as nanoparticles are well dispersed on DEG without overlapping and stacking. [ABSTRACT FROM AUTHOR]
Subject terms:
IMAGE processing - PARTICLE size determination - NANOPARTICLES analysis - TRANSMISSION electron microscopes - MAGNETIC particlesContent provider:
Computer Source
A microscope image processing method for analyzing TLIPSS structures
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Компьютерная оптика, Vol 43, Iss 6, Pp 936-945 (2019)
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Dmitrij Belousov;Alexander Dostovalov;Victor Korolkov;Sergey Mikerin
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Компьютерная оптика, Vol 43, Iss 6, Pp 936-945 (2019)
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The paper describes a method for processing microimages of thermochemical laser-induce...
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A microscope image processing method for analyzing TLIPSS structures
Компьютерная оптика, Vol 43, Iss 6, Pp 936-945 (2019)
The paper describes a method for processing microimages of thermochemical laser-induced periodic surface structures (TLIPSS) to quantify their structural order and defects. Results of its application for the analysis of microimages of periodic structures formed in 30-nm chromium films by an astigmatically focused femtosecond Gaussian laser beam have been presented. Dependences of the relative area of the beam-modified region, the area of defects, and the ordering of the periodic structures on the scanning speed and the writing beam power have been obtained.
Subject terms:
digital image processing - microscope image processing - laser materials processing - laser-induced periodic surface structures - Information theory - Q350-390 - Optics. Light - QC350-467Content provider:
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A Voronoi diagram approach for detecting defects in 3D printed fiber-reinforced polymers from microscope images
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Computational Visual Media, Vol 9, Iss 1, Pp 41-56 (2022)
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Xiang Li;Sara McMains
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Computational Visual Media, Vol 9, Iss 1, Pp 41-56 (2022)
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Abstract Fiber-reinforced polymer (FRP) composites are increasingly popular due to the...
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A Voronoi diagram approach for detecting defects in 3D printed fiber-reinforced polymers from microscope images
Computational Visual Media, Vol 9, Iss 1, Pp 41-56 (2022)
Abstract Fiber-reinforced polymer (FRP) composites are increasingly popular due to their superior strength to weight ratio. In contrast to significant recent advances in automating the FRP manufacturing process via 3D printing, quality inspection and defect detection remain largely manual and inefficient. In this paper, we propose a new approach to automatically detect, from microscope images , one of the major defects in 3D printed FRP parts: fiber-deficient areas (or equivalently, resin-rich areas). From cross-sectional microscope images , we detect the locations and sizes of fibers, construct their Voronoi diagram, and employ α-shape theory to determine fiber-deficient areas. Our Voronoi diagram and α-shape construction algorithms are specialized to exploit typical characteristics of 3D printed FRP parts, giving significant efficiency gains. Our algorithms robustly handle real-world inputs containing hundreds of thousands of fiber cross-sections, whether in general or non-general position.
Subject terms:
3D printing (3DP) - microscope image processing - fiber-reinforced polymer (FRP) - Voronoi diagrams - α-shapes - resin-rich areas - Electronic computers. Computer science - QA75.5-76.95Content provider:
Directory of Open Access Journals
A Rapid Detection Method for Fungal Spores from Greenhouse Crops Based on CMOS Image Sensors and Diffraction Fingerprint Feature Processing .
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Journal of Fungi. Apr2022, Vol. 8 Issue 4, p374-374. 17p.
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Wang, Yafei;Mao, Hanping;Xu, Guilin;Zhang, Xiaodong;Zhang, Yakun
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Journal of Fungi. Apr2022, Vol. 8 Issue 4, p374-374. 17p.
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A novel transfer learning for recognition of overlapping nano object.
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Neural Computing & Applications. Apr2022, Vol. 34 Issue 7, p5729-5741. 13p.
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Han, Yuexing;Liu, Yuhong;Wang, Bing;Chen, Qiaochuan;Song, Leilei;Tong, Lin;...
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Neural Computing & Applications. Apr2022, Vol. 34 Issue 7, p5729-5741. 13p.
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While the science and technology of nanostructure have rapidly been developed in many ...
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A novel transfer learning for recognition of overlapping nano object.
Neural Computing & Applications. Apr2022, Vol. 34 Issue 7, p5729-5741. 13p.
While the science and technology of nanostructure have rapidly been developed in many fields, it is still hard to obtain sufficient samples of nano objects due to high cost, thus, impeding the development of deep learning approaches to the material fields. Here, we develop a novel approach to recognize nano objects in Atomic Force Microscope (AFM) images . First, a noise reduction method based on the Laplacian of the Gaussian(LoG) is represented to denoise the AFM images . Then, two improved methods based on the watershed algorithm are proposed to segment the overlapping objects. Finally, a CNN recognition model based on transfer learning which is pre-trained on a large scale of shapes of handwritten numbers and letters is built to recognize the nano objects in AFM images . These methods can resolve effectively the small sample problem of AFM image processing . [ABSTRACT FROM AUTHOR]
Subject terms:
IMAGE recognition (Computer vision) - ATOMIC force microscopes - DEEP learning - NOISE control - IMAGE processingContent provider:
Computer Source
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